{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
     "start_time": "2025-02-06T14:04:28.531690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:12.876001Z",
     "iopub.status.busy": "2025-02-09T08:41:12.875871Z",
     "iopub.status.idle": "2025-02-09T08:41:14.809611Z",
     "shell.execute_reply": "2025-02-09T08:41:14.808964Z",
     "shell.execute_reply.started": "2025-02-09T08:41:12.875984Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.811862Z",
     "iopub.status.busy": "2025-02-09T08:41:14.811256Z",
     "iopub.status.idle": "2025-02-09T08:41:14.814203Z",
     "shell.execute_reply": "2025-02-09T08:41:14.813780Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.811829Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.814891Z",
     "iopub.status.busy": "2025-02-09T08:41:14.814737Z",
     "iopub.status.idle": "2025-02-09T08:41:14.822339Z",
     "shell.execute_reply": "2025-02-09T08:41:14.821865Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.814877Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.823077Z",
     "iopub.status.busy": "2025-02-09T08:41:14.822917Z",
     "iopub.status.idle": "2025-02-09T08:41:14.825233Z",
     "shell.execute_reply": "2025-02-09T08:41:14.824752Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.823061Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.825948Z",
     "iopub.status.busy": "2025-02-09T08:41:14.825772Z",
     "iopub.status.idle": "2025-02-09T08:41:14.850159Z",
     "shell.execute_reply": "2025-02-09T08:41:14.849765Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.825930Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1119342.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.850770Z",
     "iopub.status.busy": "2025-02-09T08:41:14.850615Z",
     "iopub.status.idle": "2025-02-09T08:41:14.859412Z",
     "shell.execute_reply": "2025-02-09T08:41:14.859032Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.850756Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.861176Z",
     "iopub.status.busy": "2025-02-09T08:41:14.860856Z",
     "iopub.status.idle": "2025-02-09T08:41:14.863070Z",
     "shell.execute_reply": "2025-02-09T08:41:14.862625Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.861160Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.863835Z",
     "iopub.status.busy": "2025-02-09T08:41:14.863555Z",
     "iopub.status.idle": "2025-02-09T08:41:14.868729Z",
     "shell.execute_reply": "2025-02-09T08:41:14.868350Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.863819Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1dd026c49a90114",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.363561Z",
     "start_time": "2025-02-06T14:04:29.355876Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.869508Z",
     "iopub.status.busy": "2025-02-09T08:41:14.869278Z",
     "iopub.status.idle": "2025-02-09T08:41:14.875916Z",
     "shell.execute_reply": "2025-02-09T08:41:14.875541Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.869493Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71ae51fb9433db98",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.444646Z",
     "start_time": "2025-02-06T14:04:29.441583Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.876894Z",
     "iopub.status.busy": "2025-02-09T08:41:14.876528Z",
     "iopub.status.idle": "2025-02-09T08:41:14.879567Z",
     "shell.execute_reply": "2025-02-09T08:41:14.879038Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.876865Z"
    }
   },
   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "24e99727ef49605",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.533317Z",
     "start_time": "2025-02-06T14:04:29.526671Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.880591Z",
     "iopub.status.busy": "2025-02-09T08:41:14.880204Z",
     "iopub.status.idle": "2025-02-09T08:41:14.884983Z",
     "shell.execute_reply": "2025-02-09T08:41:14.884488Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.880563Z"
    }
   },
   "outputs": [],
   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.571687Z",
     "start_time": "2025-02-06T14:04:29.566838Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.886058Z",
     "iopub.status.busy": "2025-02-09T08:41:14.885680Z",
     "iopub.status.idle": "2025-02-09T08:41:14.889011Z",
     "shell.execute_reply": "2025-02-09T08:41:14.888469Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.886031Z"
    }
   },
   "outputs": [],
   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "847a70137968848a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.819755Z",
     "start_time": "2025-02-06T14:04:29.650702Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:14.890037Z",
     "iopub.status.busy": "2025-02-09T08:41:14.889796Z",
     "iopub.status.idle": "2025-02-09T08:41:15.014902Z",
     "shell.execute_reply": "2025-02-09T08:41:15.014330Z",
     "shell.execute_reply.started": "2025-02-09T08:41:14.890010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.015737Z",
     "iopub.status.busy": "2025-02-09T08:41:15.015546Z",
     "iopub.status.idle": "2025-02-09T08:41:15.096705Z",
     "shell.execute_reply": "2025-02-09T08:41:15.096023Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.015719Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.097538Z",
     "iopub.status.busy": "2025-02-09T08:41:15.097316Z",
     "iopub.status.idle": "2025-02-09T08:41:15.102750Z",
     "shell.execute_reply": "2025-02-09T08:41:15.102288Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.097511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.103420Z",
     "iopub.status.busy": "2025-02-09T08:41:15.103246Z",
     "iopub.status.idle": "2025-02-09T08:41:15.108635Z",
     "shell.execute_reply": "2025-02-09T08:41:15.108191Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.103402Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.1388, 0.8229, 0.0383],\n",
      "        [0.1712, 0.5795, 0.2494]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.109427Z",
     "iopub.status.busy": "2025-02-09T08:41:15.109194Z",
     "iopub.status.idle": "2025-02-09T08:41:15.311554Z",
     "shell.execute_reply": "2025-02-09T08:41:15.310986Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.109401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.312730Z",
     "iopub.status.busy": "2025-02-09T08:41:15.312259Z",
     "iopub.status.idle": "2025-02-09T08:41:15.530321Z",
     "shell.execute_reply": "2025-02-09T08:41:15.529884Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.312698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAAG6CAYAAAC/RrTYAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAiFJJREFUeJzs3Xd4FOX2wPHvbja99xBI6L2TQOhdOgpYwBpRriigP8RyL4oCXr3YFZVrQ8GGIiKgKL230DsJkJCEEEjvvez8/ggsLNlAEmZ3Ifd8nmcfzeSd2XN2Zg9nZ97ZaBRFURBCCCGEuEVaawcghBBCiLpBmgohhBBCqEKaCiGEEEKoQpoKIYQQQqhCmgohhBBCqEKaCiGEEEKoQpoKIYQQQqhCmgohhBBCqEKaCiGEEEKoQpoKcVt6/PHHadSoUbXGzpkzB41Gc9Nx/fv3p127drcYmboaNWrE448/bu0wLKYm+/V2UN1j69qxaWlpZo7K/B5//HFcXFysHYa4A0lTIe4IBQUFzJkzh61bt1o7lNvaf/7zH1auXFlp+e7du5kzZw5ZWVlmj+HixYvMmTOHI0eOmP25rKGq11gIIU2FuEMUFBQwd+5ck03FrFmzKCwstHxQt6EbNRVz5861WFMxd+5ck03F119/zenTp80eg1pMHVvSVAhRNZ21AxDiVul0OnQ6OZTvBLa2ttYOoUbk2BKiZuRMhai2K9eMz5w5wyOPPIK7uzu+vr689tprKIpCQkIC99xzD25ubgQEBPDBBx8Yrb948WI0Gg1xcXFGy7du3YpGo6ny0kZcXBy+vr4AzJ07F41Gg0ajYc6cOUZxVdepU6cYMGAATk5O1K9fn3fffbfSmOLiYmbPnk2zZs2wt7cnKCiIl19+meLiYqNxixYtYuDAgfj5+WFvb0+bNm34/PPPK21PURTefPNNGjRogJOTEwMGDODkyZPVjvn999+nZ8+eeHt74+joSEhICL/99pvRGI1GQ35+Pt99953hNXr88ceZM2cOL730EgCNGzc2/O7a/fDjjz8SEhKCo6MjXl5eTJgwgYSEBKPtX5mTcqPXb+vWrXTt2hWAiRMnGp5r8eLFgOk5Ffn5+bzwwgsEBQVhb29Py5Ytef/997n+DyhrNBqmTZvGypUradeuHfb29rRt25a1a9fe8LVTFAUfHx9mzJhhWKbX6/Hw8MDGxsbo7M0777yDTqcjLy8PqHxsVfUaXysrK4vHH38cDw8P3N3dmThxIgUFBTeMEa6+vseOHaNfv344OTnRrFkzw37etm0bYWFhODo60rJlSzZu3Gi0fnx8PFOmTKFly5Y4Ojri7e3N/fffX+n9Vlpayty5c2nevDkODg54e3vTu3dvNmzYcMP4jhw5gq+vL/379ze8PkJcT5oKUWPjx49Hr9fz9ttvExYWxptvvsnHH3/MXXfdRf369XnnnXdo1qwZL774Itu3b7/l5/P19TX8Qz127Fh++OEHfvjhB8aNG1fjbWVmZjJs2DA6duzIBx98QKtWrfjnP//JmjVrDGP0ej13330377//PqNHj+bTTz9lzJgxfPTRR4wfP95oe59//jkNGzbklVde4YMPPiAoKIgpU6awYMECo3Gvv/46r732Gh07duS9996jSZMmDBkyhPz8/GrFPX/+fDp37swbb7zBf/7zH3Q6Hffffz9//fWXYcwPP/yAvb09ffr0MbxGkydPZty4cTz44IMAfPTRR4bfXWnU3nrrLR577DGaN2/Ohx9+yPTp09m0aRN9+/atdLnkZq9f69ateeONNwB46qmnDM/Vt29fk3kpisLdd9/NRx99xLBhw/jwww9p2bIlL730klETcMXOnTuZMmUKEyZM4N1336WoqIh7772X9PT0Kl87jUZDr169jI7FY8eOkZ2dDcCuXbsMy3fs2EHnzp2rnKRY1Wt8rQceeIDc3FzmzZvHAw88wOLFi5k7d26V8V0rMzOTUaNGERYWxrvvvou9vT0TJkxg6dKlTJgwgREjRvD222+Tn5/PfffdR25urmHd/fv3s3v3biZMmMAnn3zC008/zaZNm+jfv79RUzNnzhzmzp3LgAED+Oyzz3j11VcJDg7m0KFDVca1f/9+Bg4cSOfOnVmzZo1M4hRVU4SoptmzZyuA8tRTTxmWlZWVKQ0aNFA0Go3y9ttvG5ZnZmYqjo6OSnh4uGHZokWLFECJjY012u6WLVsUQNmyZYthWXh4uNKwYUPDz6mpqQqgzJ49u8q4bqZfv34KoHz//feGZcXFxUpAQIBy7733Gpb98MMPilarVXbs2GG0/hdffKEAyq5duwzLCgoKKj3P0KFDlSZNmhh+TklJUezs7JSRI0cqer3esPyVV15RAKPXqCrXP09JSYnSrl07ZeDAgUbLnZ2dTW7vvffeM/nax8XFKTY2Nspbb71ltPz48eOKTqczWl7d12///v0KoCxatKhSHNfv15UrVyqA8uabbxqNu++++xSNRqNER0cblgGKnZ2d0bKjR48qgPLpp59Weq7r87exsVFycnIURVGUTz75RGnYsKHSrVs35Z///KeiKIpSXl6ueHh4KM8//7xhPVPHVlWv8ZWxTzzxhNHysWPHKt7e3jeMT1Guvr5LliwxLIuKilIARavVKhEREYbl69atq/QamzoW9+zZU2mfdezYURk5cuQNYwkPD1ecnZ0VRVGUnTt3Km5ubsrIkSOVoqKim+Yh/rfJmQpRY5MmTTL8v42NDaGhoSiKwpNPPmlY7uHhQcuWLTl37pw1QqySi4sLjzzyiOFnOzs7unXrZhTnsmXLaN26Na1atSItLc3wGDhwIABbtmwxjHV0dDT8f3Z2NmlpafTr149z584ZPglv3LiRkpISnn32WaNT6dOnT6923Nc+T2ZmJtnZ2fTp0+eGny6r4/fff0ev1/PAAw8Y5RoQEEDz5s2NcoXqvX418ffff2NjY8Nzzz1ntPyFF15AURSjM0gAgwcPpmnTpoafO3TogJub202fv0+fPpSXl7N7926g4oxEnz596NOnDzt27ADgxIkTZGVl0adPn1rlcsXTTz9d6bnT09PJycm56bouLi5MmDDB8HPLli3x8PCgdevWhIWFGZZf+f9r8772GCktLSU9PZ1mzZrh4eFhdJx4eHhw8uRJzp49e9N4tmzZwtChQxk0aBC///479vb2N11H/G+TpkLUWHBwsNHP7u7uODg44OPjU2l5ZmamJUO7qQYNGlSaf+Hp6WkU59mzZzl58iS+vr5GjxYtWgCQkpJiGLtr1y4GDx6Ms7MzHh4e+Pr68sorrwAYmor4+HgAmjdvbvS8vr6+eHp6Vivu1atX0717dxwcHPDy8jJcErryHLV19uxZFEWhefPmlfKNjIw0yhWq9/rVRHx8PIGBgbi6uhotb926teH317r+2Kvu83fp0gUnJydDA3Glqejbty8HDhygqKjI8LvevXvXKpeqYryyj6vzGpl6fd3d3QkKCqq07PptFhYW8vrrrxvmpvj4+ODr60tWVpbRcfLGG2+QlZVFixYtaN++PS+99BLHjh2rFEtRUREjR46kc+fO/Prrr9jZ2d00fiFkWrOoMRsbm2otA4wm21U1mbK8vFydwKqhOnHq9Xrat2/Phx9+aHLslQIfExPDoEGDaNWqFR9++CFBQUHY2dnx999/89FHH6HX61WJeceOHdx999307duX//73v9SrVw9bW1sWLVrEkiVLbmnber0ejUbDmjVrTL421187r87rZ061fX5bW1vCwsLYvn070dHRJCUl0adPH/z9/SktLWXv3r3s2LGDVq1aGeaaWDrGG61bnW0+++yzLFq0iOnTp9OjRw/c3d3RaDRMmDDB6Fjs27cvMTExrFq1ivXr17Nw4UI++ugjvvjiC6OzkPb29owYMYJVq1axdu1aRo0addP4hZCmQljMlU9s10/+u/7TqCk1ubvjVjVt2pSjR48yaNCgGz7vn3/+SXFxMX/88YfRp9PrLxk0bNgQqDgr0KRJE8Py1NTUan16Xb58OQ4ODqxbt87o9POiRYsqja0q3qqWN23aFEVRaNy4seFMzK2qyb5q2LAhGzduJDc31+hsRVRUlOH3aunTpw/vvPMOGzduxMfHh1atWqHRaGjbti07duxgx44d1fqH05LHYk389ttvhIeHG911VVRUZPK7Sby8vJg4cSITJ04kLy+Pvn37MmfOHKOmQqPR8NNPP3HPPfdw//33s2bNGvr372+BTMSdTC5/CIu5ci382ln45eXlfPXVVzdd18nJCajckJjDAw88QGJiIl9//XWl3xUWFhru2Ljy6fHaT4vZ2dmV/rEfPHgwtra2fPrpp0ZjP/7442rFY2Njg0ajMTqjExcXZ/ILmJydnU2+Rs7OzkDl12/cuHHY2Ngwd+7cSp+kFUW54V0VVanquUwZMWIE5eXlfPbZZ0bLP/roIzQaDcOHD6/x81elT58+FBcX8/HHH9O7d29Dc3DlTo6LFy9Waz5FVa+xtdnY2FTah59++mmlM4HX71MXFxeaNWtW6XZpqJgz8/vvv9O1a1dGjx7Nvn371A9c1ClypkJYTNu2benevTszZ84kIyMDLy8vfvnlF8rKym66rqOjI23atGHp0qW0aNECLy8v2rVrZ5a/5fHoo4/y66+/8vTTT7NlyxZ69epFeXk5UVFR/Prrr6xbt47Q0FCGDBmCnZ0do0ePZvLkyeTl5fH111/j5+fHpUuXDNvz9fXlxRdfZN68eYwaNYoRI0Zw+PBh1qxZU2keiikjR47kww8/ZNiwYTz00EOkpKSwYMECmjVrVulaeEhICBs3buTDDz8kMDCQxo0bExYWRkhICACvvvoqEyZMwNbWltGjR9O0aVPefPNNZs6cSVxcHGPGjMHV1ZXY2FhWrFjBU089xYsvvlij169p06Z4eHjwxRdf4OrqirOzM2FhYTRu3LjS2NGjRzNgwABeffVV4uLi6NixI+vXr2fVqlVMnz7daFLmrerRowc6nY7Tp0/z1FNPGZb37dvXcMtydZqKql5jaxs1ahQ//PAD7u7utGnThj179rBx40a8vb2NxrVp04b+/fsTEhKCl5cXBw4c4LfffmPatGkmt+vo6Mjq1asZOHAgw4cPZ9u2bbfd39ARtxEr3HEi7lBXbplLTU01Wn7t7WfX6tevn9K2bVujZTExMcrgwYMVe3t7xd/fX3nllVeUDRs23PSWUkVRlN27dyshISGKnZ2d0e2lNbml9Pp4qnqukpIS5Z133lHatm2r2NvbK56enkpISIgyd+5cJTs72zDujz/+UDp06KA4ODgojRo1Ut555x3l22+/rXT7Znl5uTJ37lylXr16iqOjo9K/f3/lxIkTSsOGDat1S+k333yjNG/eXLG3t1datWqlLFq0yGTeUVFRSt++fRVHR8dKt6v++9//VurXr69otdpK8S1fvlzp3bu34uzsrDg7OyutWrVSpk6dqpw+fbpWr9+qVauUNm3aKDqdzujWR1Njc3Nzleeff14JDAxUbG1tlebNmyvvvfee0e23ilJxS+nUqVMrPX91X0NFUZSuXbsqgLJ3717DsgsXLiiAEhQUVGl8TV7jqt4fVd1Kfb2qXt+GDRuavAX0+tcjMzNTmThxouLj46O4uLgoQ4cOVaKioiq9Pm+++abSrVs3xcPDQ3F0dFRatWqlvPXWW0pJSYlhjKn3dFpamtKmTRslICBAOXv27A1zEf+7NIpioRlWQgghhKjTZE6FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVRRp5uKBQsW0KhRIxwcHAgLC6tTf2Fv+/btjB49msDAQDQajcm/WHmnmjdvHl27dsXV1RU/Pz/GjBnD6dOnrR2Waj7//HM6dOiAm5sbbm5u9OjRgzVr1lg7LHGNulo76nLdAKkdt4M621QsXbqUGTNmMHv2bA4dOkTHjh0ZOnQoKSkp1g5NFfn5+XTs2JEFCxZYOxTVbdu2jalTpxIREcGGDRsoLS1lyJAhhj85fqdr0KABb7/9NgcPHuTAgQMMHDiQe+65h5MnT1o7NEHdrh11uW6A1I7bgrX/opm5dOvWzegv+JWXlyuBgYHKvHnzrBiVeQDKihUrrB2G2aSkpCiAsm3bNmuHYjaenp7KwoULrR2GUP53akddrxuKIrXDGurkmYqSkhIOHjzI4MGDDcu0Wi2DBw9mz549VoxM1EZ2djYAXl5eVo5EfeXl5fzyyy/k5+fTo0cPa4fzP09qR90itcPydNYOwBzS0tIoLy/H39/faLm/vz9RUVFWikrUhl6vZ/r06fTq1Yt27dpZOxzVHD9+nB49elBUVISLiwsrVqygTZs21g7rf57UjrpDaod11MmmQtQdU6dO5cSJE+zcudPaoaiqZcuWHDlyhOzsbH777TfCw8PZtm3bbVUchLiTSe2wjjrZVPj4+GBjY0NycrLR8uTkZAICAqwUlaipadOmsXr1arZv306DBg2sHY6q7OzsaNasGQAhISHs37+f+fPn8+WXX1o5sv9tUjvqBqkd1lMn51TY2dkREhLCpk2bDMv0ej2bNm26ra49CdMURWHatGmsWLGCzZs307hxY2uHZHZ6vZ7i4mJrh/E/T2rHnU1qh/XVyTMVADNmzCA8PJzQ0FC6devGxx9/TH5+PhMnTrR2aKrIy8sjOjra8HNsbCxHjhzBy8uL4OBgK0Z266ZOncqSJUtYtWoVrq6uJCUlAeDu7o6jo6OVo7t1M2fOZPjw4QQHB5Obm8uSJUvYunUr69ats3ZogrpdO+py3QCpHbcFa99+Yk6ffvqpEhwcrNjZ2SndunVTIiIirB2SarZs2aIAlR7h4eHWDu2WmcoLUBYtWmTt0FTxxBNPKA0bNlTs7OwUX19fZdCgQcr69eutHZa4Rl2tHXW5biiK1I7bgUZRFMWSTYwQQggh6qY6OadCCCGEEJYnTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVRR55uK4uJi5syZc1t9jamaJL87W13P705V1/eL5Hdnu53zq/NffpWTk4O7uzvZ2dm4ublZOxzVSX53trqe352qru8Xye/OdjvnV+fPVAghhBDCMqSpEEIIIYQqLP5XSvV6PRcvXsTV1RWNRmP258vJyTH6b10j+d3ZLJ2foijk5uYSGBiIVnvnfKaQuqEuye/OdjvXDYvPqbhw4QJBQUGWfEohxHUSEhJo0KCBtcOoNqkbQlhfdeqGxc9UuLq6AvDixoHYO1v86S3iRe9oa4dgVmNbtLd2CKKWyihlJ38b3od3iivx3rNqArbOdlaOxjy+Dt5p7RDMSurGnasmdcPi/6pfOXVp76zDwcXW0k9vEW6ud85p5drQaermfvufcPm8pCUuIajpSry2znZ1tqmQuiFuWzWoG3X7KBZCCCGExUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVeisHUBNhXiNpIfPOFx0niQXxbLu0pdcLDxz0/XauPdlXNDLnM7Zw7LzbxmWj64/nY6eg43GxuQe5Of42arHXi1OD6NxngRaXyiNQsl9A0qPmR7rOA6t+ztGixSlGCW5neFnjfs7aBzHGY8p3o6S+aTqoVfH3VOGcv+Ld+MV4EHM0XgWPPctp/dHVzm+733dCX9jAgGNfEk8m8TCf/3IvjWHjcaEzx3P8EmDcPFw5uSuKD6Z8jWJ0UnmTsWkup7fnewu//6MChyKu6075wsS+C72Z2Ly4266Xg/vrjzb/CkOZBzmwzP/NSy/t8Foenh3xcvOi3KljNj8eJYmrCQmL9aMWdyAyrVDG3DW5Kr6nHegYKFqYVdXXX9v1ZX8anWmYsGCBTRq1AgHBwfCwsLYt2+f2nGZ1MatD3cFTGJHys8sjPk/kotiebDRGzjZuN9wPXdbPwYHPMH5/BMmfx+de4CPoh4xPFYkvGuO8G/OYQQa11dQ8j5DSRsDZZFoPL8FrVeVqyj6XPQpPQwPJbVf5THF24zHZD1vxiSq1u+Bnkz+IJwf31jGMyH/5NyxeOatfRUPXzeT49v0aMErS6az9tvNPNPlZXat2secFS/TqG2QYcz4l+9hzLPDmf/MVzzbfSZF+cXMWzsLW3tbS6VlUNfzU4O1akd371AeafgAv1/4k1eP/5vz+Rf4V+vpuOlcb7iej703DwXfT2RO5Q8ulwqTWRz7M/86Noc5J98ltTidma2m46pzMVMWN2CG2nHt7/QpPdBn/wtF0UPxOjMnU1ldf2/Vpfxq3FQsXbqUGTNmMHv2bA4dOkTHjh0ZOnQoKSkp5ojPSJjPGA5nruNo1kbSihP4++ICSvXFdPK8q8p1NGgZ0+BFtqf8RGaJ6Q6tXCklvyzL8CjS55srhRvSOD0BBUuhcDmUR6PkvA5KITjed4O1FNCnXfNINzGkxHiMkmO2HG7k3udHsWbhJtYt3sr5yAvMf/origtKGPrEQJPjxz43kv1rj7Ds/T84H5XId68vJfrQOe6ZNuzqmP8byU9vLWfPHweIPX6ed8I/wzvQk15juloqLYO6nt+tsmbtGFHvLrak7GBb6m4SCy/xTeyPFOtL6OfXq8p1NGiY2mwSyy/8QUpxWqXf707fx4mcSFKK00gsvMiP8b/ipHMi2KmBOVMxHas5aofR79LQ2A+CkggoTzBrLqbU9fdWXcqvxk3Fhx9+yD/+8Q8mTpxImzZt+OKLL3BycuLbb781R3wGWo2Oeo7NiM07cs1Shbi8I9R3alXlen38JpBfns2RzA1Vjmno3J7nW/3IM82/YHi9KTja3PjTi3nYgm1blJLd1yxToGQ3GtvOVa+mcULjuxWN73Y0Hp+DrlnlMXZhaHwj0PisQ+M2FzQeagd/UzpbHS1CmnBo49XTsYqicGjjMdp0b2FynTY9WnBok/Hp2wPrj9L68viAxn541/Pk8Mbjht8X5BQQtTeaNj1amiGLqtX1/NRgrdpho7GhsXNDTmRHGpYpKJzIjqS5S9Mq1xvXYDQ5pblsTd1ZrecY6NeX/LICzhdcUCXu6jNj7bhC6w32/VEKf1Mt6uqq6++tupZfjZqKkpISDh48yODBV+cgaLVaBg8ezJ49e1QP7lpONm5oNTbkl2UZLc8ry8JF52lynSCnNnTyHMJfiZ9Wud2YvEOsuvAhP8a+yqakxQQ7t2NCw7loLD2HVeuJRqOr+FRwrfL0imukppSdQ8meiZL5DErWi4AWjdevoA0wDFGKt6Nkv4SS+RhK7ntg1w2N50IsPUfX3ccVG50NmcnZRsszU7LxDPAwuY5ngAdZ149PzsLr8vgr/81Mzqo0xtPf9DbNpa7nd6usWTtcdS7YaGzILjU+Q5ddmoOHnenTyy1dm9HftzcLz31/w2139ujAt10/5btu/2V4vcHMi/yI3LI81WKvFjPVDiOO40DJhyLLX/qo6++tupZfjSZqpqWlUV5ejr+/v9Fyf39/oqKiTK5TXFxMcXGx4eecHMucerfTOnJPgxn8lfgpheVVP+ep7O2G/08tjielKJZpLb+hoXN74vKPWiLU2is9UvG4TMk6hMZnLRqnCSh5H1csLPrr6viyMyhlp9H6bkaxC4MS8xZzIa6oae2wVt0AcNDa80zTJ1kY+/1NG4RTOVHMPPYGrrauDPDrw3PNJ/P6if+QU5ZroWhrqTq14xoax3uh8A+gxFIRijuU2T+uzps3D3d3d8MjKCjo5iuZUFCeg14px1nnYbTcRedBXllmpfGedgF42AUwvuHrvNJ2Fa+0XUUHj4G0cA3jlbar8LQz3ZFnlSaTX5aNl129WsVZa/pMFKUMtD7Gy228QZ9azY2UQdkpsGlY9ZDyBBR9xo3HmEF2Wi7lZeV4+htPqvX0cyczKcvkOplJWXhcP97fg4zL46/89/rO29Pfo1KHbm51PT9LU6tuAOSW5VGulONua3xWwt3WjaySys2Kv4Mffg4+vNhyGj+EfcEPYV/Qx6c7XTw78kPYF/jZX/30X6wvIbk4lei8c3x97jv0Sjn9/XrXOtZaMXftsA1Fo2uKUrjslkOtjbr+3qpr+dWoqfDx8cHGxobk5GSj5cnJyQQEmP5HeubMmWRnZxseCQm1m+SjV8q4VBhNY5eO1yzV0MilI4kFlT/ppBVf4MuzU/k6+jnD40zuXuLyj/N19HNkl1aeeAXgqvPGycaV3LKMWsVZe6VQehKNXY9rlmnAridK6eEq1zKmBV0L0N9g4ps2oGJOxY3GmEFZaRlnDp6j86D2hmUajYbOg9pzKsL0LcGn9pyh88D2Rsu6DO5A5OXxSbEppF/KpPOgq7fBObk60iqsGaf2nDZDFlWr6/ndqprWDrXqBkC5Uk5sfjxt3VsblmnQ0NatNWfzYiqNv1h4iZePzmbmsTcMj0OZRzmVc5qZx94gvaTq2qDRaLDVWvpOffPWDo3T/Silx6HM9Nloc6vr7626ll+Nmgo7OztCQkLYtGmTYZler2fTpk306NHD5Dr29va4ubkZPWprb9pKOnsOpYPHQLztGzAicAq2WgeOZm4E4O76MxjgHw5U3NGRWhxv9Cgqz6dEX0BqcTx6pQxbrQOD/CdS37El7rZ+NHLuyAMNXyOj5BLn8g7VOs7aUgq+Bafx4DAWbJqicXsDNI4VM7oBjfu7aFxeuLqC8zSw6w02QaBrg8b9A7Cpj1Jw+ROFxgmN6z/BthPY1Ae7Hmg8P4fyeCi++eQztS3/aDUjJg3irsf6EdyqPs99/g8cnO1Zt2gLAC8vnsYT/3nIMH7FJ3/RdVgn7psxiqCWgTw6+35ahDZl1Wdrr46Z/xcPvXovPUaH0qhdMC9/N430i5nsWrlf8ruN1LR2qFk3AP6+tIEBfn3o49ODQIcAnmj8MA42dmxL3QXAM02fYHzQWABKlTIuFF40euSXF1JUXsSFwouUK+XYa+0YHzSWZi5N8LHzorFzME81CcfTzpOI9IO3FGttqF47rtC4gP2wysstrK6/t+pSfjVuqWfMmEF4eDihoaF069aNjz/+mPz8fCZOnGiO+IycytmBU5I7/fwewVnnSXLROX6Oe5388iwA3O18UdBXe3uKosfPoTEdPAfhoHUmtyyDc3mH2Zb8I+VKmZmyuIGiv1G0Xmhc/+/yF9hEVnxJ1ZVbvWwCAcUwXKN1A/c3K8bqs6H0JEr6eCi//IUpSjnoWqLxGAta14pPIcU7L18ztfy10W2/7sbD143wuePxDPAg5kgcrwx/i6yUiglHfsE+KPqr+Z3ac4Z5D8/n8X8/yMS3HiLx7CXmjH2XuJNXP7UufXcVDs4OTP9yMi4eTpzYGcXM4W9RWlwq+d1mrFk7ItIP4KZz5b6ge/CwdSO+IIG3o+aTU1ox98Hb3gv9Ne+tm9Ereuo5BjDdtweuOhfyyvKJyYvjjZPvklh40VxpVE3t2nGFw0jQaKDoT8vlYkJdf2/Vpfw0iqJU/5102WeffcZ7771HUlISnTp14pNPPiEsLKxa6+bk5ODu7s6re4bg4HJnfoHPzbzic2edmq6poYGdrB2CqKUypZStrCI7O/uWP/3XRm1rx5W6cd/Gx7B1trNApJb3Y6Ot1g7BrKRu3LlqUjdqdfFv2rRpTJs2rVbBCSH+d0ntEKJukz8oJoQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghV6Kz1xKsX9MPGzsFaT29WP47sau0QzOrts79bOwSzWtC8hbVDEFXYt78FWoe6WTeaRDexdghm5fd3jrVDMCv3EdHWDuG2IGcqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCp21A6ip+wd05LFhoXi7O3M2IZV3l2zhZGySybFNAr15ekxPWjf0I9DHnfd/3sLPGw8bjfnznScJ9HGvtO6vm4/wzk+bzZLDjTzUpCtPNu+Jj4MLUdlJvHl0DcczL5oce1dgKya37EOwsxc6rZb4vAwWnd3DHwnHTI6f02kkE5qE8p+ja/k+Zq8506hSY7fxNPcIx97Gh+ySMxxLe5us4hM3Xa++yzC6+r/DpfzN7E163rDcRuNIW+/p1HMegJ3WnfyyRM5l/0xczjJzplGlu6cM5f4X78YrwIOYo/EseO5bTu+PrnJ83/u6E/7GBAIa+ZJ4NomF//qRfWuMj9HwueMZPmkQLh7OnNwVxSdTviYx2vQxL6r2aIdOPBUaiq+TM5FpqczZspmjyaZfxwnt2jOudRtaePsAcDwlmfd37TQa7+PkxD9796FPcCPc7O3Zl3iBOVs3E5eVZYl0Knm0RRcmtwnD19GFyMwUZu9fz9H0SybHDg1qwdR2PWnk6olOqyUuJ5OvI/exIvbqe3F6h96MbtiGes6ulJaXczwjifePbOdIuul6ZG73BXfnkcZ98bZz4WxuEu9H/sGp7Asmx/b3b8vEJv1p4OSNTmNDQkEaP8XtZM3Fq+8tRxs7prYYRj//NrjbOnGxMINf43fze8I+S6VkpK7Ujhqfqdi+fTujR48mMDAQjUbDypUrzRCWaXd1bcGM8f346o8IHp77I2cSUvns+XF4ujqaHO9gpyMxNZtPl+8kLSvP5JhH/72EIc9/YXg88/5vAGw8cMZseVRleP22/Kv9EBZEbWPc5i85nZ3Mwl6P4GXvZHJ8dkkhX5zewYRt33DPpi/4Pf4I/wm5h95+TSuNHRzYio5eDUguzDF3GlWq7zyUdj4vEpX5JVsvTCCn5DQ9632OnY3XDddz0gXSznsGaYUHK/2uvc+L+Dn15GDyK2xKGEtM1k908PkXAU79zJVGlfo90JPJH4Tz4xvLeCbkn5w7Fs+8ta/i4etmcnybHi14Zcl01n67mWe6vMyuVfuYs+JlGrUNMowZ//I9jHl2OPOf+Ypnu8+kKL+YeWtnYWtva6m0VGHNugEwskVLXu3bj/kRexi15AciU1P5buy9eDuarh1hDYL443QUDy7/lXFLf+ZSbi7fj7sXf2cXw5gvR99DsJsHT/25klFLfiAxN4cfx92Po87yn9VGNWzNrJBBzD+2k5F/f8upzGS+Hzge7yprRxELTuxm7NrvGbb6G5bFHOO9HiPpW6+xYcy5nAxe37+eoau/4b71P3IhP5vvB43Hy970a2ZOgwPaM73VSBZGb+Kx3Z9xNvcSn4Q+gaeds8nxOaUFLIrZwpMRn/PQrvn8mXiQ19rdS3ef5oYx01uNpIdPC2YfW8r4nR/yS9wuXmx9N318W1sqLYO6VDtq3FTk5+fTsWNHFixYYI54buiRISGs2H6CP3edJPZSBv/5YSNFJWXc07udyfGn4pKZv2w76/edpqSs3OSYrLxC0nMKDI8+HZuQkJzFwdOmO2Bzerx5d5bFHeL3+CPE5KYx+/BqispLubdhZ5Pj96XFs/FiFOdy00jIz+SHmL2czkmmi0+w0Tg/B1dmdRzOS/t/p0yvt0QqJjX1eJT4nN85n7uK3NJzHEl9k3KliIauY26wlpYQv/8QlfE5BaWV94mXQycScv8kregABWUXic9dTk7JGTwdTB8T5nTv86NYs3AT6xZv5XzkBeY//RXFBSUMfWKgyfFjnxvJ/rVHWPb+H5yPSuS715cSfegc90wbdnXM/43kp7eWs+ePA8QeP8874Z/hHehJrzFdLZWWKqxZNwAmdQlh6Ynj/HbqJNEZGby6aQOFZaXc37a9yfHPr/2bH48dJTI1lXOZGfxr43o0aOgVXPHeauzhSZd6gczavJFjycmcy8xk1qaN2Ot03N3S8v8oTWrdjV+ij7Ls3HGis9N5de9aCsvLeKBZB5PjI5LPsy7hDDE56ZzPy2LR6QNEZaUQ6nf1H6U/4k6xKymOhLwszman8ebBTbjZOdDK089SaRk81KgPKxP2szrxILH5Kbx9ciVF5SWMrh9qcvyhjFi2ppwiLj+VxMIMlsbvJjo3iY4ejQxjOngE89fFQxzKiOVSYRYrL+znbG4SbT0aWCirq+pS7ahxUzF8+HDefPNNxo4da454qqSz0dKqoT/7IuMNyxQF9p2Kp33Teqo9x4jurVm18+an49Vmq9HS1iOQ3SnnDMsUYE/KOTp5Ve8g7+7bmMYu3hxIu/oaaYB3Q8fyzZndROemqhx19WnQ4WHfmtSCiGuWKqQWRuDlYLrwAbTynExxeSbxuStM/j6j6AgBTv1wsKkodD4OXXG2bUhKwR41w78pna2OFiFNOLTx6qUnRVE4tPEYbbq3MLlOmx4tOLTJ+FLVgfVHaX15fEBjP7zreXJ443HD7wtyCojaG02bHi3NkIX5WKtuANhqtbTz82dnwnnDMgXYdf48XepVr3Y46nTY2mjJKioCwM7GBoDi8jKjbZaUlxNaP1C12KvDVqulnVcAuy7FGsWy61IcXXzqV2sbPQMa0sTNi33J503+3lar5cFmncgpKSIyM0WNsKtNp7GhlVsg+9OvXgpQUNifHkN7j+AbrHlVV6+mNHT25XDm1dfoWNZ5+vq1xte+4mxAiFcTgp192Jt2Vt0EbqKu1Y47Zk6Fh6sjOhst6TkFRsvTcwpoVO/Gp8+ra0DnZrg42fPn7pOqbK8mPO2d0Gm1pBfnGy1PK86nsatPleu56OzZNmIGdlob9IrC3CN/GTUm/2jRm3JFzw9WmkNxhb2NJ1qNjqLydKPlxWXpuDg2NrmOl0NnGrqNZUvCA1Vu91jq23Tye51hjTagV0pRUDiSMpf0okOqxn8z7j6u2OhsyEzONlqemZJNUCvThd0zwIOs68cnZ+EV4AFg+G9mclalMZ7+HmqE/T/B09ERnVZLWsF1762CApp6Va92/LN3X5Lz8tl5vqJhj8nMIDEnh5d79eGVTRsoLC3liS4hBLq64nfNJRJLuFI70oqMa2NqUT5N3b2rXM/V1p6IcdOws6moHbP2rWNnUpzRmIH1m/Fp73tw1NmSUpjHI5t+IbO40BxpVMnDzgmd1oaMEuNL2BnFuTR09q1yPWedPX/1n4mdVke5oufdU6vYd01j8v6pP3il3Tj+GjCTMn05ehT+c+J3DmfGmSsVk+pa7TB7U1FcXExxcbHh55wc613Tv5l7+rRj9/FY0rLybz74NpFfVszYTV/gpLOjh28T/tV+KBfyM9mXFk9bj3o82iyMezd/ae0wa0yncSLE7y0Op86lRJ9V5bgm7g/iad+BiEvPUVB2EW+HEDr4vkJReSqphdZtpETt3U514+nQboxu2ZIHf/uVkvKKy6hlej1Pr17FO3cN5egz0yjT69l1Pp4tsefQaDRWi7Um8kqLGfHXtzjb2tIzoBGvhQwiIS+LiGvOVuxJimfEX9/i5eDIhGadWNBnDGPWfEd6ccENtnx7KCgr4ZHdn+JoY0dX76ZMbzWSxMIMDmVUnK14oGFP2nkEMePgdyQVZtHZqzEvtbmH1OIc9qfHWDn6O5fZm4p58+Yxd+7cW95OVm4hZeV6vN2MJx55uzmRln3rTUCAtyvd2gTz0oI/b3lbtZFZXECZXo+3vfHEIx97Z9KKTE8yhYrTnOfzMwGIyk6miZsPT7Xszb60eEK8g/G2d2bzsKt3S+i0Wv7ZYQjhzbozaN18s+RiSnF5JnqlDAcb409O9jpvisvTKo13tg3C2bY+3QM+MSzTXL5ad3eTg2w8fw9F5am08X6OvUnPk1ywA4CckrO427ekmUe4RZuK7LRcysvK8fQ3vpPI08+dzKQsk+tkJmXhcf14fw8yLo+/8t9rl135OeZonEqR357UqhsAmYWFlOn1+Dhd995yciI1/8a14x9dQnmma1ceWf4bUWnGx+mJlBRG/vQDrnZ22NrYkFFYyIoJD3E8OVmVuKvrSu3wcTCujb4OzqQW3rh2xOdV1I5TmSk0c/dmStseRk1FYXkp8XmZxOdlcjjtIlvunsz4Zh3570nLXV7MKimgTF+Ol53xGSAve1fSi3OrXE9B4UJBxZnRs7mXaOzsx+NN+nMoIxZ7rY4pLYbw8uEf2ZV6GoDovCRauNbjkUZ9LdpU1LXaYfbvqZg5cybZ2dmGR0JCQq22U1auJyo+ma6tr15D02iga+tgjseYvm2qJu7u1Y7MnAJ2Hjt388FmUKroOZl1kR5+TQzLNEB3vyYcyaj+pFEtGuy0Fb3iHwnHuGfT54zd/IXhkVyYwzdndjNp149qp3BDCmVkFUfi6xR2zVINvo5hZBRVvgU2tzSWTQn3suXCeMMjqWAraYX72XJhPIVlSWjRodXYomA8+VRR9IYGxFLKSss4c/AcnQddnfin0WjoPKg9pyJM30l0as8ZOg80nijYZXAHIi+PT4pNIf1SJp0HXZ106uTqSKuwZpzac9oMWdw+1KobAKV6PSdSkukVdE3tAHoGBXPoUtW1Y3JIV6aFdSd8xe8cT6m6UcgtKSGjsJBGHh609/NnQ0zVtwGaQ6lez4mMJHoGNDIs01AxT+JQWmK1t6NFY5grUuUYzc3HqK1MKScq5yJdva/e1aZBQ6h3U45nmZ4DYopGo8H2cm3UaWyw1erQK4rRmHL0Fj/TVNdqh9nPVNjb22Nvb6/Ktn5cf5C5Tw4jMi6ZE7FJPDS4C472tvyxq2IOxNwnh5Gamcdnv+8EKiZeNgms+GRsq7PBz9OVFkG+FBSXciEly7BdjQbu7t2W1btPUa5XKj2vpSw+G8HboWM4kXmRY5mJhDfrjqONLb/HHwHg7ZAxpBTl8uHJTQA81aI3J7Iucj4vAzsbHf38m3N3cAfmHvkLgKySQrJKjK9/lun1pBXlEZtnPLfBEmKyfqCL37/JLD5JZtEJmro/go3GkfO5KwHo4vcmRWUpnMr4BL1SQm6JcXEuLc8FGwzLy5Qy0gr30857Bsf0xRSUXcLHMYRg11EcT3/f0umx/KPVvLx4KmcOxHB6XzRjp4/EwdmedYu2APDy4mmkXczg21eWALDik7/4YOtc7psxir1/HaL/hF60CG3Kx5OvXq5aMf8vHnr1XhLPJnEpNoXH3xhP+sVMdq3cb/H8LEnNugGw8NBBPhgyjGPJSRxNSuKJLl1wsrXlt1MVk7I/GDKMpPw83ttVUTsmh3bl+e49mb72by7kZOPjVHEWoKC0lILSUgBGNG9BemEBF3NyaeXjw+v9B7A+Jpod5+NNB2FGCyP38UHPURzPSOJI2kWebN0VJ50ty2IqGvYPeo4iuSCXd49sA2BK2x4cS79EfF4WdlobBtRvytgm7Zi1bx0Ajja2TGvfk40XzpJSmIenvROPtehCgJMrf8VHWTy/JXE7mN3+fiKzEzmZncCERr1wtLFjdWLFbeZz2t9PSnEO/z1TEX94k35EZidyoSAdO62Onr4tGRHYmXdOrQQgv7yYgxnneK7lcIr1pYbLHyMCuzA/6i+L51eXakeNm4q8vDyio68W+9jYWI4cOYKXlxfBwdWbiVtbG/afwdPViafH9MTbzYkzCak8+9HvZFyevBng5YpyTefp6+HCz3MeNfz82LBQHhsWyoGoBCa/d/XLkcLaNKSet5tV7vq41prEk3jZO/Fsm/742rsQmZ3EP3b9ZJi8GejkjsLV/Bx1trzeaQQBjm4UlZcRm5vGy/tXsCbR8hNNqyMxfx126Z609pyCvc6H7OLT7Lk0heLyDACcdAGg1OyW1/3J/6SN1/8R4j8PO60bBWWXOJXxmVW+/Grbr7vx8HUjfO54PAM8iDkSxyvD3yIrpWJClV+wD8o1TeupPWeY9/B8Hv/3g0x86yESz15izth3iTt59VP50ndX4eDswPQvJ+Pi4cSJnVHMHP4WpcWlFs/vVlizbgD8deY03o6OzOjRCx8nJyLTUnl85XLSCipqR6CbG/pr3luPdOiIvU7H56PuNtrOxxG7mR9Rcerfz9mZV/v2N1xG+T3yJJ/ujcAaVsdH4mXvxPMd+uDr6ExkZgrhm381TN6s7+xmVBsddbb8u9tQ6jm5UlReRkxOOs/v+pPV8ZEA6BU9Td28ubdvezztHckqLuRY+iXuX/8jZ7MrX640t41Jx/G0c+Gp5oPxtnflTM4l/u/AIsPkTX9HD6P952hjx8tt7sHPwZ3i8lLi81N5/dhSNiZdvRti1pGfmdJiKG90GI+brRNJhZl8cXY9yxMsPxerLtUOjaIoNfpovnXrVgYMGFBpeXh4OIsXL77p+jk5Obi7u9PxkbewsXOoyVPfMfJHVn2dry54u+Pv1g7BrBY0N30bV11QppSylVVkZ2fj5mb6i3XMQa26ETzvTbQOdbNuKD4l1g7BrPx8bt9J+mpwH2HZy16WVJO6UeMzFf3796eGfYgQ4n+c1A0h/jfIHxQTQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCp01npiRVfxqItKo12tHYJZTc970NohmJX9HHtrh2A25UVFMG+VtcOoNc9TGmzsNNYOwyzy6ztYOwSzSi6r259hs1/vae0QzKa8uAjerl7dqNt7WQghhBAWI02FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVKGzdgA19UD/jjx2Vyje7s6cuZDKu79s4WRcksmxTep588zdPWkd7Eegjzvv/7qFJZsOG43RajRMHt2DEWGt8XZzJjU7jz93n2Th33stkU4lj3TuyD+6heLr7ExkSipzN27hWJLp/MZ3aM/Ytq1p4esDwImkZN7fvqvS+KZeXrzcvw9hQQ2w0WiJTk9nyso/uZSba/Z8rvdoyy5MbhuGr6MzkRkpzN63gaPpl0yOHRrcgqntetDIzROdRktcbiZfn9rHinMnAdBptLzYuS/96zch2MWD3NJidl6K551DW0kpzLNkWgYPhXbkyZ4h+Lo4E5Wcyr/XbOH4xWSTY+/v3I4xHdvQ3NcbgJOXUvhw884qx88dMYgJoR34z7qtfLf3sMkxomr3D+jIY0MrasfZhFTe/XkLJ2OrqB2B3jx9T09aN7xcO37Zws8bTdSOu3swvHtrvN2dScu6XDtWW6d2PNSt4tjzuXzsvfn3Fo4nVnHshbTjno5taO53+di7mMJHm3YajZ83ZghjO7c1Wm/H2Tj+8eMK8yVxA4+16sxT7a6pHXs3cjTNdO0YFtyCqR2609DNE1uNltjcTL4+sd+4dnTpw4AGTQl2ca+oHRfjefvgNuvVjq4debLX5dqRdLl2VLX/ulyuHX7X1A4T+29cp+v2X3Qck8y8/2p0pmLevHl07doVV1dX/Pz8GDNmDKdPnzZXbJUMCW3BjPv68dVfETz01o+cvZDKgufG4enqaHK8g52OxLRsPlmxk9Rs0wfK48O6cl+/jrzz82bunbOYT37fQfjQrkwY0NmcqZg0slULXhnQj092RXD3dz8SlZrK4gfG4e1kOr+w4Ab8GXmah39Zxn0//syl3Fy+e2Ac/i4uhjHBHu4sfXg859IzeOjnXxm5+Hs+2xNBSXmZpdIyGNWoFbNCBzL/6E5Grl7EqcwUvh88Hm8HJ5Pjs4uLWHB8D2PX/MCwP79lWfRx3us5kr6BjQFw1NnS1sufT4/tZtRfi3l66wqaunmxcMC9lkzLYHibFswc0pcF2yIY+9VPRCWl8c3D4/Cqav81asBfJ6J47PvfmPDtL1zKyeXbR8bh5+pcaezglk3p2CCA5BzrFLxbZe3acVfXFsx4oB9f/RnBw2/8yJmEVD6bfpPakZrNp8t3kpZl+jUPH96V+/p35N0lm7nvtcV8snwHjw3ryoRBlq8dw9u24F9D+7JgawTjvvyJ00lpLHx0HF7OpvPr1qgBfx2PInzxb0xY+AtJObl882jlY2/72Vh6v/el4fHCb39bIp1KRjVqxayuA5l/ZBej/lhMZEYKP9z1QJW1I6ukkM+O7WHcXz8y9I9FLDt7nPd7j7imduho5x3AJ0d3M/LP75i8ZSVN3L34ZtA4S6ZlMLxtC2Ze3n9jv/yJqOQ0vnmk6v1nqB3f/caEb37hUnYu31ax/3q9/6XhMcMC+69GTcW2bduYOnUqERERbNiwgdLSUoYMGUJ+fr654jPy8OAQVuw8wR+7TxJ7KYO3ftpIUUkZ9/RsZ3L8qfhkPl6+nfUHTlNaWm5yTMcmgWw7EsPOE7FcSs9h06GzRJyKp13jAHOmYtIToSEsPXaC5SdOEp2ewax1GyksLeO+9qbzm7F6DT8dOUpkSirnMjKZuXYDGo2Gng2DDGNe6NOLredieWfbDk6lpHI+K5tN0edILyi0VFoGk1p345ezR1kWc5zo7HRejVhLYXkpDzTrYHJ8RPJ51iWcISY7nfN5WSyKOkBUZgqhfg0AyC0t5tGNS/krPopzORkcTrvI6/vW08GnHoHObpZMDYCJPbrw66ET/H70FDFpGcz+ayNFpWXc29n0/ntxxVqWHDhGVHIq59IzmfXnBrQaDT0aBxuN83N15rXhA3hxxVpK9aaP49udtWvHI3eFsGLHCf7cVVE7/vPj5drRu4raEZfM/N+2s37/aUrKqqgdTQPZeiSGnccv146DZ4k4GU9bK9SOx3t2YdnBE/x+5BQxqRnMXn3jY++l5Wv5ef8xopJSiU3LZNaqy8deE+Njr6SsnLS8AsMjp6jYEulUMqltV345c5Rl0cc5m53OK3vWUVhWygPN25scH5GUwLrzZ4nOTud8bhaLIg8SlZlCV/8rtaOER9Yv5a+4y7Uj9SKvR2y4XDtcLZkacE3tqOb+e/H3tSy5vP/OpWUy648q9l+55fdfjZqKtWvX8vjjj9O2bVs6duzI4sWLOX/+PAcPHjRXfAY6Gy2tg/3ZGxlvWKYosDcqng5N6tV6u0fPXaRbqyCC/TwAaN7Ah07NAtl1IvZWQ64RW62WdgH+7I67Jj9gd3w8nQOrl5+jrQ5brQ1ZRUUAaID+TZsQl5HJovvHsW/q0yx/5EHuatbUDBncmK1WSzvvAHZdijMsU4Bdl+Lo4lu/WtvoGdCQJm5e7EtOqHKMq509ekUhp6ToFiOuGVutlrb1/Nkde96wTAF2x56nc4Pq7z+d1obswquxa4D3xgzjm90HiU5NVzlqy7F27WjV0J99p4xrx77IeNrfSu2IuUi31kEE+3sAl2tH80B2H7dw7bC5fOydu+bYU2DPufN0CqrBsWdjfOxBxRmNXS9NZs2z4cweNRAPRwdVY68OW62W9t4B7LxkXBt31qB29KpXUTv2JlWndli2cbK10dI2sPL+233u1moHVOy/3S9NZu20cOaMtMz+u6U5FdnZ2QB4eXmpEsyNeLg4orPRkpFbYLQ8I6eARgG1f/5Fa/fh7GDH73MnUq7osdFoWbBqJ2v2Rd1qyDXi6eSITqslrcA4v7T8AppU8/V9uV8fkvPy2BVXcXB6OzvhYmfH5LBufLhzF+9u20Hfxo3479i7efiXZexLuKB6HlXxtHeqyK/Q+JNpamE+Td28q1zP1daeiPumYmdjg15RmLV3PTuvaUyuZa+14V9dBvBH7CnySkvUDP+mruy/9Hzj/ZeeX0ATH89qbePFQX1Iyc0zKi7/6NWVMr3C9/vq1hwKa9SO9Jzr9s0t1o7Fa/bh4mjH8n9PRK/Xo9Vq+e+KnazZa4XaYaMlPe+62pFXQONqHnsv3FX52NsRHcf6yGgSM7MJ8vLg+UG9+OqRsUxY+At6RVE1hxupqnakFRbQ1P1GtcOOvQ9U1I5yReG1PTeoHTY2zAzpzx/nrFg78m6hdlSx/zZERnPh8v6bMagXXz8ylvFm3n+1bir0ej3Tp0+nV69etGtn+hQNQHFxMcXFVzu/nJyc2j6lWdwV0pLh3Vrzyjd/c+5iOi2DfHnhgf6kZuWzOuKUtcOrtslhXRnVqhUP/fIrJeUVp2u1Gg0AG6NjWHTgEACRKal0qR/IQ506WLSpqK280mJGrP4WZ50dPes14rXQgSTkZhGRfN5onE6j5bN+Y9AAs/aus06wt+Afvboyol1LHvtumWH/ta3nx2NhnRn31U9Wjk5d1akdt3vdALgrtCXDwlrz6tcVtaNFkC8vTOhPanY+q3ffObXjH70vH3uLlxld6vn7xBnD/59JSed0chobpz9Bt0YNiIit+hP/7SKvtIThfyzC2daOXvUaMqvbQM7nZRFx3dkKnUbLgn73oNHAqxHrrRRt7dVk/236P/Pvv1o3FVOnTuXEiRPs3LnzhuPmzZvH3Llza/s0Bll5hZSV6/FyNZ6Y4+XmRHp27a/LTr+3L4vX7WP9gYpJY9EX0wjwdmPi8G4WbSoyCwop0+vxcTLOz8fZidSbXHee1DWEp8O68tivyzmdmma0zdLycqLTjU+bx6RnEFo/UL3gqyGzuKAiP0fjiUS+js6kFlWdnwLE52YBcCozhWbu3kxp392oqagoCmNo4OzOgxuWWPyTBlzdf97OxvvP29mJtOs+gVzviR4hPNUrlIk//M7plKv7LzS4Pt7OTmyZPsmwTKfV8s+7+vJYWGcGffKtuklYSHVqh1p1A67WDm+36/aNmxNpt1A7/u/+vixes4/1+y/XjsQ06l2pHRZsKjILLufncl3tcKnGsdczhH/0DuWJ73/nTHLaDcdeyMwmI7+Aht4eFm0qqqodPo5OpBZWs3ZkXKkdPYyaCp1Gy4L+91DfxZ0H1/1s3drhUova0TOEp3qHMvH73zld3f3nZd79V6vvqZg2bRqrV69my5YtNGjQ4IZjZ86cSXZ2tuGRkFC7ZMrK9USeT6Zb66sTUTQa6NYqmGPnTN9WVB0Odjr0euNTQXq93vAp31JK9XpOJCXTs+E1+QE9GgZz+GLV+T3VLZRpPbszcdkKjicZ335UqtdzPCmZxl7Gp9Aae3qSmGPZ20lL9XpOpCfRs14jwzINFfMkDqUmVns7Wo0GO+3VXvhKQ9HI1ZOHN/xMVrFl51JcUarXc/JSMj0aX50kqwF6NA7i8IWq99+knqFM6RPGpJ9WcOKS8f5bdSySu7/4gTFf/mh4JOfk8c2eg0z6yTq39d2q6tYOteoGVNSOqPhkul5XO7q2Cub4LdYORalcOzSWrh3ll4+9Jtccexro3jiIIwlV5/dkr1Ce6RfGP35cwYkqbmO+lr+bCx6OjqTkWmZy7RWlej3H05PoVa+hYZkG6FWvUc1rh42N4ecrDUVjN08eXveL9WpHuZ6TF6+rHRro0eQmtaNXKFP6hjGpJvvPyZHUPPPuvxqdqVAUhWeffZYVK1awdetWGjdufNN17O3tsbe3r3WA1/pp40HmPj6MU3HJnIxL4qFBXXC0s+WP3RX3Hr/x+DBSsvL4bGXFJyCdjZYm9SquudnqbPDzcKVFA18Ki0tJSM0CYPuxczw5IoykjFxiLqXTKsiPRwaHsOryNi3p2wMHeW/EMI4nJXP0UhITQ7vgZGvLb8crYnl/xDCS8vJ4f3tFfk9168r03j14fvUaLuRk43P5U3JBSSkFpaUAfL3vAPPvHsn+hEQizifQt3EjBjZrwkM//2rx/BZG7uODXqM4nnaJI+mXeLJ1KE46O5ZFHwPgg16jSC7I5d3D2wCY0q47x9KTiM/NxM5Gx4D6TRnbpC2zIioub+g0Wj7vP5a2Xv48ufk3bDRafB0qPs1klRRSqtdbNL9Few7xzpihnLiYwrGLSYSHdcbR1pbfj1Tsv3fuGUpybh4fbt4FwD96hvJc/x688PsaErNyKu2/rMIisq6beFWqLyctL5/Y9EyL5naralo71KwbAD9uOMjcJ4YRGZ/MidgkHhrcBUd7W/7YVbFv5j4xjNSsPD77/ZraEXhd7QjypaC4lAspWQDsOHqOJ0aEkZSeS8zFdFoF+/HwkBBW7bR87Vi8+xBvjx3KicQUjiUmEd6jM452tvx+uCKWt8cOJSU3jw83Vhx7k3qH8tyAHrz42+Vjz+WaY6+kFCc7W6b27876U2dJyysgyNOdl4b04XxGFjuj46uMw1wWntzPB31GciwtiaNpl3iiTShOOluWnT0OwIe9R5JUkMu7h7YDMKV9d46lVdQOexsdAxo0YWzTtszaU3F5Q6fR8vmAMbTz9ueJjb9ho9Xie/lMSFaxlWrH2Mu1IzGJ8O6Xa8fl/ffO2KEk5+Tx4abLtaNXxf57YXnV+29av+6si7xm/93Vh/iMLHaYef/VqKmYOnUqS5YsYdWqVbi6upJ0+UuW3N3dcXQ0fT+tmtYfOIOnixPP3N0TbzcnTl9IZdonvxsmbwZ4uRpNQPH1cOGX1x41/PzYkFAeGxLKgdMJPPXhMgDe/WUzU+7pxcyHBuHp6kRqdh7Ldxzjq9URZs/nen9FncHL0YnpvXvi4+xEZEoqE5f9TvrlyZv13Izze7hzB+x1Ov47ZrTRdubv2sMnu/YAsP5sNK+t38gz3bvx+qABnMvIYOrKPzmYeNFyiV22Oi4KL3snnu/Ux/AFNuGblpJWVJFffWc3o09+jjpb/h02hHpOrhSVlxGTnc7zO/9kdVzFRLgAJ1fuCmoOwJrRTxg914R1SyrNuzC3NafO4OXsyHP9e+Dr4kRkciqTlqwwTN6s5268/yaEdsBOp+PTB4z336fb9vDZNssff+Zk7dqxYX9F7Xj6noracSYhlWc//p2My5M3A7xdjY49Xw8Xfp59Te0YFspjwypqx+T3LteOJZt5Zkwv/vVIRe1Iy8pj+bZjfP2n5ffdmpMVx96zAy8fe0mp/OOHq8deoLtxfg9ePvY+mWB87H22ZQ+fbY2gXK+npb8PYzq2wdXBntTcPHbFnGf+5t2Ullv+tubVcVF4Ozgxo3NvfB2dOZWRwmMbfjXUjkAXN/Rczc9JZ8ubPe66pnZkMH376qu1w9mFIcEVtWPtPca1Y/zaJZXmXZjblf333ICr+2/SjzeoHV0v147x19WOrVf3Xwt/H8Z0qth/KRbcfxrl+vN3NxpcxWm9RYsW8fjjj1drGzk5Obi7u9Ph8bewsbP87UmWkG35OzYtqjzA8tcdLck+Tr1PyLeb8qIiYua9QnZ2Nm5ulvsuj1utHVfqRsdH627dyK9v2csmllbYuG7XDYc4O2uHYDblxUXEvF29ulHjyx9CCFFTUjuE+N8gf1BMCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCqkKZCCCGEEKqQpkIIIYQQqpCmQgghhBCq0Fn6CRVFAaC8pMjST20x+rqbGgD6whJrh2BW5UWKtUMwG31xxcF55X14p/hfqBvlxRprh2BWdb5uFOutHYLZ1KRuaBQLV5cLFy4QFBRkyacUQlwnISGBBg0aWDuMapO6IYT1VaduWLyp0Ov1XLx4EVdXVzQa83fmOTk5BAUFkZCQgJubm9mfz9IkvzubpfNTFIXc3FwCAwPRau+cq59SN9Ql+d3Zbue6YfHLH1qt1iqfkNzc3OrkwXWF5Hdns2R+7u7uFnkeNUndMA/J7852O9aNO+ejihBCCCFua9JUCCGEEEIVdb6psLe3Z/bs2djb21s7FLOQ/O5sdT2/O1Vd3y+S353tds7P4hM1hRBCCFE31fkzFUIIIYSwDGkqhBBCCKEKaSqEEEIIoQppKoQQQgihijrdVCxYsIBGjRrh4OBAWFgY+/bts3ZIqtm+fTujR48mMDAQjUbDypUrrR2SaubNm0fXrl1xdXXFz8+PMWPGcPr0aWuHpZrPP/+cDh06GL64pkePHqxZs8baYYlr1NXaUZfrBkjtuB3U2aZi6dKlzJgxg9mzZ3Po0CE6duzI0KFDSUlJsXZoqsjPz6djx44sWLDA2qGobtu2bUydOpWIiAg2bNhAaWkpQ4YMIT8/39qhqaJBgwa8/fbbHDx4kAMHDjBw4EDuueceTp48ae3QBHW7dtTlugFSO24LSh3VrVs3ZerUqYafy8vLlcDAQGXevHlWjMo8AGXFihXWDsNsUlJSFEDZtm2btUMxG09PT2XhwoXWDkMo/zu1o67XDUWR2mENdfJMRUlJCQcPHmTw4MGGZVqtlsGDB7Nnzx4rRiZqIzs7GwAvLy8rR6K+8vJyfvnlF/Lz8+nRo4e1w/mfJ7WjbpHaYXkW/4NilpCWlkZ5eTn+/v5Gy/39/YmKirJSVKI29Ho906dPp1evXrRr187a4ajm+PHj9OjRg6KiIlxcXFixYgVt2rSxdlj/86R21B1SO6yjTjYVou6YOnUqJ06cYOfOndYORVUtW7bkyJEjZGdn89tvvxEeHs62bdtuq+IgxJ1Maod11MmmwsfHBxsbG5KTk42WJycnExAQYKWoRE1NmzaN1atXs337dqv82WtzsrOzo1mzZgCEhISwf/9+5s+fz5dffmnlyP63Se2oG6R2WE+dnFNhZ2dHSEgImzZtMizT6/Vs2rTptrr2JExTFIVp06axYsUKNm/eTOPGja0dktnp9XqKi4utHcb/PKkddzapHdZXJ89UAMyYMYPw8HBCQ0Pp1q0bH3/8Mfn5+UycONHaoakiLy+P6Ohow8+xsbEcOXIELy8vgoODrRjZrZs6dSpLlixh1apVuLq6kpSUBIC7uzuOjo5Wju7WzZw5k+HDhxMcHExubi5Llixh69atrFu3ztqhCep27ajLdQOkdtwWrH37iTl9+umnSnBwsGJnZ6d069ZNiYiIsHZIqtmyZYsCVHqEh4dbO7RbZiovQFm0aJG1Q1PFE088oTRs2FCxs7NTfH19lUGDBinr16+3dljiGnW1dtTluqEoUjtuB/Knz4UQQgihijo5p0IIIYQQlidNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVRR55uK4uJi5syZc1t945iaJL87W13P705V1/eL5Hdnu53zq/PfU5GTk4O7uzvZ2dm4ublZOxzVSX53trqe352qru8Xye/OdjvnV+fPVAghhBDCMqSpEEIIIYQqLP4HxfR6PRcvXsTV1RWNRmP258vJyTH6b10j+d3ZLJ2foijk5uYSGBiIVnvnfKaQuqEuye/OdjvXDYvPqbhw4QJBQUGWfEohxHUSEhJo0KCBtcOoNqkbQlhfdeqGxc9UuLq6AlB/7iy0Dg6WfnqLODb2W2uHYFZjW7S3dgiilsooZSd/G96HdwpD3Zj/T7SO9laOxjyO9f/R2iGYldSNO1dN6obFm4orpy61Dg5oHetmU+HmeuecVq4NncbW2iGI2rp8XtISlxDUZKgbjvZSN+5QUjfuYDWoG3X7KBZCCCGExUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghVSFMhhBBCCFVIUyGEEEIIVUhTIYQQQghV6KwdQE092r4Tk7uE4uvkTGRaKrO3b+ZocpLJsUObNmNqSBiNPDzQaW2Iy8rk68MHWHE60uT4t/oP5uH2HXlj+xa+PXrInGlUzelhNM6TQOsLpVEouW9A6bGqx2tc0bjMAIchoPWA8kSUnLegZNvVMVp/NK4vgX1f0DhCWTxK9r+g7ITZ07ne3VOGcv+Ld+MV4EHM0XgWPPctp/dHVzm+733dCX9jAgGNfEk8m8TCf/3IvjWHjcaEzx3P8EmDcPFw5uSuKD6Z8jWJ0aaPCXOr6/ndyR5t0YXJbcLwdXQhMjOF2fvXczT9ksmxQ4NaMLVdTxq5eqLTaonLyeTryH2siL36nol7ZKbJdf9zaDNfndprlhxuSO3a4fgQGqcHwaZBxc9lZ1HyPoOS7WZPxZS6/t6qK/nV6kzFggULaNSoEQ4ODoSFhbFv3z614zJpVPOWzOrTj/n79jDylx84lZbK93ffi7ejo8nx2UVFLDiwl7HLfmbYku9YFnmC9wYPo29ww0pjhzZpRueAeiTl5Zo7jao5jEDj+gpK3mcoaWOgLBKN57eg9apiBVs0XovBpgFK1rMoaUNQsmeBPvnqEI0bGu9fQClDyZyEkjYcJfdtUHIskJCxfg/0ZPIH4fz4xjKeCfkn547FM2/tq3j4upkc36ZHC15ZMp21327mmS4vs2vVPuaseJlGbYMMY8a/fA9jnh3O/Ge+4tnuMynKL2be2lnY2ttaKi2Dup6fGqxWOxq2ZlbIIOYf28nIv7/lVGYy3w8cj7e9k8nx2SVFLDixm7Frv2fY6m9YFnOM93qMpG+9xoYxXX/7xOjx0u7V6BWFNedPWyQnI+aoHfoklNz3UdLHoKSPhZI9aDw/B10zCyRkrK6/t+pSfjVuKpYuXcqMGTOYPXs2hw4domPHjgwdOpSUlBRzxGdkUqcQfjl5nGWRJ4nOzODVLRsoLCvlgTbtTY6PSLzAunPRxGRmcD4nm0VHDxOVlkpovfpG4/ydXZjTbyD/t/5vyvR6s+dRFY3TE1CwFAqXQ3k0Ss7roBSC432mV3C8DzQeKFnPQOkhKE+E0n1QFnV1m85PQfkllJx/VXxqKb8AJTuh/LyFsrrq3udHsWbhJtYt3sr5yAvMf/origtKGPrEQJPjxz43kv1rj7Ds/T84H5XId68vJfrQOe6ZNuzqmP8byU9vLWfPHweIPX6ed8I/wzvQk15juloqLYO6nt+tsmrtaN2NX6KPsuzccaKz03l171oKy8t4oFkHk+Mjks+zLuEMMTnpnM/LYtHpA0RlpRDqd7VopxblGz3uCmrBnqR4EvKyzJ7P9cxROyjeXHHWojweyuNQ8j4CpQBsO1kkp2vV9fdWXcqvxk3Fhx9+yD/+8Q8mTpxImzZt+OKLL3BycuLbb781R3wGtlot7fz82ZVw9R9DBdiVcJ4uAfWqtY2eDYJp4unFvouJhmUa4KO7hvPVof2czUhXOeqasAXbtiglu69ZpkDJbjS2nU2uoXEYCKWH0bjNRuO7B433X+D8NEa71WEQlJ5A4/EJGt8INN6rwPEBs2Ziis5WR4uQJhzaePV0rKIoHNp4jDbdW5hcp02PFhzaZHz69sD6o7S+PD6gsR/e9Tw5vPG44fcFOQVE7Y2mTY+WZsiianU9PzVYtXZ4BbDrUqxhmQLsuhRHF5/6Va94jZ4BDWni5sW+ZNPNuI+DEwPqN2VpzFE1Qq4hM9UOI1pwGAkaJyg5onL8N1bX31t1Lb8azakoKSnh4MGDzJx59VqiVqtl8ODB7Nmzx+Q6xcXFFBcXG37OyandaXdPR0d0Wi1pBflGy1MLCmjqWdUpPnC1syNi4mTsbGzQKwqztm5iZ0K84ffPhHSjTNGz6OjhKrdhEVpPNBodij7NeHl5Otg1Nb2OTRDY9YDCP1AyJ4GuIRq3OSjoIP+zq2OcHoL8b1HyvgDb9mjcXkNRSqFohXlzuoa7jys2Ohsyk7ONlmemZBPUynRh9wzwIOv68clZeAV4ABj+m5mcVWmMp7+HGmFXW13P71bVtHaoVTcAPO2dKmpHUYHR8tSifJq6e1e5nqutPRHjpl2tHfvWsTMpzuTYe5u0J7+0hHXWuPRhrtoBoGuBxutX0NiDUoCSOQXKq77Obw51/b1V1/KrUVORlpZGeXk5/v7+Rsv9/f2Jiooyuc68efOYO3du7SO8RXklJYz45QecbW3pGRTMa336kZCTRUTiBdr5+jGxYxdGLv3BavHdGi3o01FyZgF6KDuJovVH4zwJxVAYNFB6AiXvw4ofy05VFAqnB1Es2FSI/201rR3WrhsAeaXFjPjr24raEdCI10IGkZCXRYSJsxUPNO3IytiTFOvLrRBpbVSndgBlsSjpd1dM6nQYhsbjXZT0hy3eWIg7h9lvKZ05cybZ2dmGR0JCQq22k1lYSJlej4+Ts9FyXycnUq87e3EtBYjPzuJUWioLDx/k7+izTAkJA6BbYAO8nZzY/fhTRE99nuipz9PAzZ1Xe/djZ/ikWsVZa/pMFKUMtD7Gy228QZ9axTqpUBYLXDMPpCwGjY0fYHvNGOMCoJTFgE31LhmpJTstl/Kycjz93Y2We/q5k5mUZXKdzKQsPK4f7+9BxuXxV/57feft6e9RqUM3t7qen6WpVTcAMosLKmqHg/GkTF8HZ1IL86pcTwHi8zI5lZnCwsh9/H0+iilte1Qa19W3AU3dvVkabY1LH5ivdgBQWjH/quwkSt4HUBqJxjlc5QRurK6/t+pafjVqKnx8fLCxsSE5OdloeXJyMgEBASbXsbe3x83NzehRG6V6PSdSkunZINiwTAP0DArmUJLp28JM0Wo02NnYAPD76VMMW/IdI37+3vBIysvlq8MHeGzV8lrFWXulUHoSjd21RUsDdj1RSqu4NFNyEHQNK8ZdoWuMUp5csT2AkkOga2y0mkbXCMovqhj7zZWVlnHm4Dk6D7o6qVaj0dB5UHtORZwxuc6pPWfoPNB4Em6XwR2IvDw+KTaF9EuZdB7UzvB7J1dHWoU149Qey56Gruv53aqa1g616gZcrh0ZSfQMaGRYpqFinsShtMQq17uelqu141rjm3XkWPolIrPMP+HUNDPVDpO0oLG79ZBroK6/t+pafjVqKuzs7AgJCWHTpk2GZXq9nk2bNtGjR+UOXm0LjxzkwbbtubdVG5p6evHWgME46WxZdqri3vEP7hrGyz16G8ZPCelG76CGBLm509TTi0mdQxjbsrXheyqyioo4k5Fu9CjT60nNz+dcVqbZ87meUvAtOI0Hh7Fg0xSN2xsV3ytRWNHgaNzfRePywjXjl4DGA43rLLBpBPb90Tg/jVLw09Ux+YsqZms7Pw02weAwGhzHG42xlOUfrWbEpEHc9Vg/glvV57nP/4GDsz3rFm0B4OXF03jiPw8Zxq/45C+6DuvEfTNGEdQykEdn30+L0Kas+mzt1THz/+KhV++lx+hQGrUL5uXvppF+MZNdK/dLfrcRq9eOyH082LwT9zZpT1M3b94KG1ZRO2IqJrt90HMUL3fqZxg/pW0Pegc0IsjFg6Zu3kxq3Y2xTdqxIvak0XZdbO0Y0bCV9c5SXGaO2qFxeQFsu4JN/YpLpi4vgF0YSuEflk6vzr+36lJ+Nf7yqxkzZhAeHk5oaCjdunXj448/Jj8/n4kTJ5ojPiOrz57Gy9GR58N64evsRGRqKuF/LCetsGICVn0XNxRFMYx3tLXl3/0HUc/FhaKyMmIyM3l+wxpWn71NP+UV/Y2i9ULj+n+Xv8AmEiXzSdBfvivFJpCKk7KX6ZNQMieicX0Vjc9qKE9GKfgO8r+6OqbsOErW1IqC4DINyi+g5L4FRZYvDNt+3Y2Hrxvhc8fjGeBBzJE4Xhn+FlkpFROO/IJ9UPRX8zu15wzzHp7P4/9+kIlvPUTi2UvMGfsucSevngpf+u4qHJwdmP7lZFw8nDixM4qZw9+itPhGn7YkP2uwau2Ij8TL3onnO/TB19GZyMwUwjf/api8Wd/5utqhs+Xf3YZSz8mVovIyYnLSeX7Xn6yON/7ivNEN26BBwx9xp8yeww2Zo3ZovdF4vAtaP9DnQlkUSuYTULLLoqlB3X9v1aX8NMq176Rq+uyzz3jvvfdISkqiU6dOfPLJJ4SFhVVr3ZycHNzd3Ql65020jg41DvhOcO7+L6wdglkNDexk7RBELZUppWxlFdnZ2bd0SaG2als7DHXjq9frbt0YbN5ba61N6sadqyZ1o1Zf0z1t2jSmTZtWq+CEEP+7pHYIUbfJHxQTQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqpKkQQgghhCqkqRBCCCGEKqSpEEIIIYQqdNZ6YreG2dg4FVnr6c2qxwtPWzsEs0r+vsTaIZhd88cOWTsEYUquLZTZWjsKs2i88ilrh2BWmgUaa4dgds2n7rV2CFYnZyqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQppKoQQQgihCmkqhBBCCKEKaSqEEEIIoQqdtQOoqfGNuhHetDc+9i6cyUni7RN/cSIr8abrDQtszzshD7A5KZLn9y8xLH+6xQCG1W9PgIM7pfpyTmVf5LOojRzPumDONKp0312deHhkKN7uzpw9n8oH323m1Lkkk2Mb1/dm8n09adnYn0Bfdz76YQu/rD1kNMbJwZbJ9/WiX9fmeLo5ciYulQ9/2EzkuWRLpFPJoy26MLlNGL6OLkRmpjB7/3qOpl8yOXZoUAumtutJI1dPdFotcTmZfB25jxWxJwxjnHS2/LPzAIY0aI6nvSMJedksPn2An84etlRKRu6eMpT7X7wbrwAPYo7Gs+C5bzm9P7rK8X3v6074GxMIaORL4tkkFv7rR/atMY49fO54hk8ahIuHMyd3RfHJlK9JjDZ9TIiqPdq2E5M7dcXXyZnI9FRm79zE0RTTr+PQxs2Z2iWMRu4e6LQ2xGVn8vXRA6w4c8owZnpoT0Y3a0k9FzdK9eUcT03m/b07OFLFNs3tsbadeapTV3wdnYlMT2H2rqrzG9a4OVM7d6ehuwe2Wi2x2Vl8fXQ/K85el1/TVgS6uFKq13M8NZn39u3gSIrp96u51fX9V1dqR43PVGzfvp3Ro0cTGBiIRqNh5cqVZgjLtKGB7XixzXC+PLOFCds/53ROEp+HheNl53zD9QIdPZjRZigH0+Mq/S4+P515x1dz77bPeHzXQi4WZPJ593A87ZzMlEXVBndvyf893I9vft9D+KwfiD6fyvx/3Yunm6PJ8Q72OhJTsvnvLztIy8wzOeaVfwylW/uGzPn8bx7+1/fsPR7HZzPvx9fTxZypmDSqYWtmhQxi/rGdjPz7W05lJvP9wPF425t+rbNLilhwYjdj137PsNXfsCzmGO/1GEnfeo0NY2aFDKJfYBOe3/0ng//8mm+j9jO36xAGN2hmqbQM+j3Qk8kfhPPjG8t4JuSfnDsWz7y1r+Lh62ZyfJseLXhlyXTWfruZZ7q8zK5V+5iz4mUatQ0yjBn/8j2MeXY485/5ime7z6Qov5h5a2dha29rqbRUYc26ATCqaUtm9erP/AN7GPnbD5xKT+H7Uffh7VjFsVdcxIJDEYz9fQnDfl3MsqgTvDdgGH2DGhnGnMvO4PUdmxi6dDH3rfiZC7nZfD/qfrwcTL9fzWlU05bM6tmf+Qd2M2r590Smp/LDyPvxdjCdX1ZxEZ8dimDcip8Yuuw7lp0+zvsDhtO3QSPDmNisTF7fuYkhvy7m3pVLuJCbzQ8jrZhfHd5/dal21LipyM/Pp2PHjixYsMAc8dzQo0168vv5A6xKOMy5vFTePPYnReWljAnuUuU6WjT8p8t9fH56MxcKMir9fk3iMfamnSOxIJOYvBTeP7UWV1sHmrsFmDMVkx4cHsKqLcdZvf0ksYkZvP3tBoqKSxndr73J8ZHnkvn05+1siDhNSVl5pd/b2+oY0LU5n/28nSNRiVxIzmLh73u4kJzFuMEdzZ1OJZNad+OX6KMsO3ec6Ox0Xt27lsLyMh5o1sHk+Ijk86xLOENMTjrn87JYdPoAUVkphPpdfeOE+DZg+bnjRCSf50J+Nj9HHyEyM5mO3oGWSsvg3udHsWbhJtYt3sr5yAvMf/origtKGPrEQJPjxz43kv1rj7Ds/T84H5XId68vJfrQOe6ZNuzqmP8byU9vLWfPHweIPX6ed8I/wzvQk15juloqLVVYs24ATOoYyi+njrPs9AmiM9N5ddsGCktLeaBVO5PjIy4msC42mpisDM7nZLPo+CGi0lMJDahvGPPH2Sh2JZ4nITebs5npvLlrK2729rTy9rVUWgaTOoTyS+Qxlp0+wdnMdF7Zvp7CspvkF3eW6KwMzudkGfLrWu9qfquiI9mVGG/I79+7t+Bmb09ra+RXx/dfXaodNW4qhg8fzptvvsnYsWPNEU+VdBobWrsHEpF2zrBMQSEiLYYOnkFVrje5xQAyi/NZkXCoyjHXPse9waHklBZyJseyp8B0NlpaNfZn34nzhmWKAvtPnKd983q12qaNjQadjZbiUuOGo7ikjI4t6lexlnnYarW08wpg16VYwzIF2HUpji4+1YulZ0BDmrh5sS/56mt0MPUCgxs0x9+x4sxLD/9gGrt5seOa57EEna2OFiFNOLTxmGGZoigc2niMNt1bmFynTY8WHNp0zGjZgfVHaX15fEBjP7zreXJ443HD7wtyCojaG02bHi3NkIX5WKtuwOVjz9efXRfiDcsUYFfiebr4V6/57Fk/mCYeXuy7ZPqyqK1Wy4NtOpBTXERkeqoaYVebrVZLe98Adl6X384L8dXOr1f9YJp4eLL3Bvk91KYj2cVFnLJCfnV5/9W12mH2ORXFxcUUFxcbfs7JyanVdjztnNBpbUgvNj7Nn16cR2MXH5PrdPYKZmxwFx7Y/t8bbruvXwveCXkABxtb0oryeHrPd2SVFNQqztrycHVEZ6MlIzvfaHlGTgENA71qtc2ColKOnbnIE2O6E5eYTkZ2AUN6tqJd83pcSMpSIerq87R3QqfVklZk/LqmFuXT1N27yvVcbe2JGDcNOxsb9IrCrH3r2JkUZ/j9nP0bmBc2nL33Pkupvhy9ojAzYg37UhLMlYpJ7j6u2OhsyEzONlqemZJNUCvTTZNngAdZ149PzsIrwAPA8N/M5KxKYzz9PdQI+7alVt0A8HRwrDj2Co3fW6kF+TT1qPq95WpnR8RjT2OnvXzs7dho9A83wMCGTfj0rlE46mxJyc/jkT9/I7OosNax1sbV/IzfW2mFBTfNb++jz2CntaFcUXhtx4bK+QU34bO7RlfkV5DHI6uXWTG/urn/6lrtMHtTMW/ePObOnWvup6nEycaOtzrfx9xjq27aIOxPj+WBbf/Fw86JexuG8l7oeB7Z8SUZJfk3XO9OMOfzv5n11FD+WvA0ZeV6Tscls353FK0a+1s7tGrJKy1mxF/f4mxrS8+ARrwWMoiEvCwiLp+tCG8ZQiffQJ7csozE/Gy6+QXzRrchJBfmseua5kPcWaxVN66VV1LCiF+/rzj2GjTktZ79ScjJJuLi1YZ1T2ICI379Hi9HRya07sCCIaMZ8/tPpBda9kNJbeSVlDB82Xc429rRq34ws3oO4HzudfldTGD4su/wcnDkwdYd+O9do7nn959IL7oz8qvL++92ZfamYubMmcyYMcPwc05ODkFBVV+uqEpmSQFl+nK87Y0nGHrbu5BWXHmSYpCzF/WdPPmk68OGZVqNBoCDI+dwz5b5XCjIBKCwvJSEggwSCjI4nnWBPwZMZ0xwCN9Gb69xnLWVlVtIWbkeL3fjSadebk6Vzl7URGJKNs+8+SsO9jqcHe1Jz8rnzWdHcTEl++YrqyizuIAyvR6f6yaO+To4k1poepIpVJzmjM+r2E+nMlNo5u7NlLY9iEg+j72Njpc69Wfy9uVsSYwBICorlTZefjzVJsyiTUV2Wi7lZeV4+rsbLff0cyezirNCmUlZeFw/3t+DjMvjr/z32mVXfo45GqdS5LcnteoGQGZRYcWx52j83vJ1cia1oOr3lgLE52QBcCo9lWaeXkzp3M3oH6XCslLic7KIz8nicPIltjz4JONbteO/h/fVKtbauJqf8XvLx9GpBvml0MzTmymdw6rOL+USWx+cxPjW7fnv4b3mSMWkur7/6lrtMPv3VNjb2+Pm5mb0qI0ypZzI7IuE+TQxLNOgIcynCccyK5/qjs1L496tnzJ++38Nj63Jp9mfFsv47f8lqbDq06lajQY7rU2t4qytsnI9UbHJdG0bbFim0UDXdsEcP3vrt3AVFZeRnpWPq5M93ds3ZPvBqm9VModSvZ4TGUn0DGhkWKahYp7EobSb3xJ8hRYNdjYV+8ZWq8XOxgZFUYzG6BUFDRo1wq62stIyzhw8R+dBVyfVajQaOg9qz6mIMybXObXnDJ0HGk/C7TK4A5GXxyfFppB+KZPOg65ORnNydaRVWDNO7TlthixuH2rVDbh87KUm07PBNe8tKq6zH0q+WO3tVBx7N/4cptXcfIzaKm73TKJX/YaGZRqgV/2GNctPc/W9VeUYbj5GbXV9/9W12nFHfU/FD+d28+9O4ziZlciJrEQeadIDRxs7Vp6vmIT5Zqd7SSnK4ZOoDZToy4jOTTFaP7e04lrZleWONrZMat6PrUlRpBXn4mHnzIRG3fBzcGXDxZOWTQ74ec1BXp88jMjYJE7FJDFhWBcc7G1Zva3iexlmPz2M1Mw8/rt0J1AxubNxg4r5CLY6G3w9XWje0JfColIuXL6WFta+IRqNhvhLGQT5e/LsQ32Jv5TBn9stn9/CyH180HMUxzOSOJJ2kSdbd8VJZ8uymIoJRx/0HEVyQS7vHtkGwJS2PTiWfon4vCzstDYMqN+UsU3aMWvfOgDySkuISI5nZpeBFJWXcSEvm+7+wYxr3I43D26yeH7LP1rNy4uncuZADKf3RTN2+kgcnO1Zt2gLAC8vnkbaxQy+faXie1JWfPIXH2ydy30zRrH3r0P0n9CLFqFN+Xjyl4Ztrpj/Fw+9ei+JZ5O4FJvC42+MJ/1iJrtW7rd4fneyhUcP8MHA4RxPTeZI8iWe7BCCk60ty6Iq3lsfDBxOcn4e7+7dAcCUzt04lppMfHYWdjY2DGjYhLEt2jBrx0YAHHW2TAsJY2NcDCn5+Xg6OPJYu04EOLvwV4zlG76Fxw7wwYARHEtN4mjKJZ7oEFqR3+mK/D4cMIKk/Fze3XclvzCOpSYRn52FvY0NA4KbMLZ5G2bt2HA1vy7d2RgXTUpBRX7h7Trjb6386vj+q0u1o8ZNRV5eHtHRVz/lxsbGcuTIEby8vAgODr7Bmrdu3cUTeNo5M6XlIHzsXTidc4kpe783zH0IcHRHj77a2ytXFBq7+HJ3aGc87JzIKi3gZFYiE3d9Q0xeys03oLKNEafxcHXkqft64e3uxJn4VKa/s5yMnIrre/7ebuiv+VTu6+nCj/95zPDzI6O68siorhw8lcCUt34FwMXJninj++Dn5UJOXhFb9p/l8193Ul5e/ddJLavjI/Gyd+L5Dn0qvqAnM4Xwzb8aJm/Wd3YzOuvgqLPl392GUs/JlaLyMmJy0nl+15+sjo80jHl2xype7tyfj3vdjYedA4n5Obx3dBs/WuHLr7b9uhsPXzfC547HM8CDmCNxvDL8LbIuX2ryC/ZB0V/N79SeM8x7eD6P//tBJr71EIlnLzFn7LvEnbx65m3pu6twcHZg+peTcfFw4sTOKGYOf4vS4lKL53crrFk3AFbHnMbL0Ynnu/bC18mJyLRUwlf/ZpjcWN/lumPP1pZ/9xlMPRcXisrKiMnK4PlNf7P68j84ekVPUw8v7h3SFk9HR7KKijiWksT9K3/hbGa62fMxlZ+3gxMzuvbC18mZU2kpPPbX1fwCXV3RczU/J50tb/a5i3rOV/Obvvkvo/yaeXhx39B78HSoyO9oyiXuX/Wz1fKry/uvLtUOjXL9ueOb2Lp1KwMGDKi0PDw8nMWLF990/ZycHNzd3Wn7y0vYONnX5KnvGA6/eFg7BLNKHl5i7RDMrvljN78F+U5UppSylVVkZ2ff0iWFmlKrbgR98G+0jg5miND6FE2NSvEdR6NY9pKkNTSfarm5JpZUk7pR4zMV/fv3r3QNWwghbkTqhhD/G+QPigkhhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhXSVAghhBBCFdJUCCGEEEIV0lQIIYQQQhU6az2x+0JndDoHaz29WcWN0Vs7BLMKWm61w8Zizs7vbu0QzEJfWAT/XGXtMGrN54AWG7u6+Vkos421IzAvjyhrR2B+0R/W0bpRVAQzq1c36ua7UwghhBAWJ02FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUIU2FEEIIIVQhTYUQQgghVCFNhRBCCCFUobN2ADU1ZlRnJtwXhpenMzHnUpj/+UaizlwyObZRsA9PPNqbFs0DqOfvzqdfbuK3lQeq3PZD94cx+Yn+LFt5gM++3GSuFG7osbadeapjV3wdnYlMT2H2rk0cTU0yOXZY4+ZM7dydhm4e2Gq1xGZn8fWx/aw4e8ow5v3+w7m/ZTuj9bYmxBL+929mzaMq44Z35sExXfHycCYmLoWPFm4i8qzp/BoHefPkg71p2dSfen7uzP9mM8tWH6w0zsfLhWce60f3Lo1xsNNxISmL/3y6htMxyeZOp5JH23dicpdQfJ2ciUxLZfb2zRxNNp3f0KbNmBoSRiMPD3RaG+KyMvn68AFWnI40Of6t/oN5uH1H3ti+hW+PHjJnGnXS/YM68sjwULzdnTl7PpX3ftzCqVjT+6ZJoDeTx/WkVSM/An3c+XDJFn5ef9hojJODLU+P60X/Ls3wdHPiTHwKHyzZwqlYyx93AI907sikbqH4OjsTmZLKGxu3cCzJdH5DmjfjmR7daHjl2MvM5Nv9B1l5yvjY+7/ePRnfoR1u9g4cTEzk9Q2biM/MskA2ld0/sCOPDru8/xJSee+nLZysYv+N6duekT1b07S+DwCR8cn8d/muSuMnj+nJ2L7tcHFy4Gh0Im9/v4mElCxzp2LSox068VTo1doxZ8uNa8eUbsa1Y+HBA6yIijQa83CHjrTz88fT0ZERP31PZGqq2fOo0ZmKefPm0bVrV1xdXfHz82PMmDGcPn3aXLFVMqBvK6Y+NZDvftrFP55dTExsCu+/+QAe7k4mxzs46LiYlMVXi7aRnpF3w223ahHA3SM6EX0uxRyhV8uopi2Z1aM/8w/uZtTy74nMSOWHkffj7WA6v6yiIj47FMG4lT8x9LfvWHb6OO/3H07fBo2Mxm09f47Q7/9reDy78U8LZFPZwF4tmTaxP4uW7ubJF74nOi6VD1+/v8r9Z29vy8XkLL74YTtpVew/V2d7Pp/3EGVl5bz479945LlFfLZoK7n5xWbMxLRRzVsyq08/5u/bw8hffuBUWirf330v3o6OJsdnFxWx4MBexi77mWFLvmNZ5AneGzyMvsENK40d2qQZnQPqkZSXa+40zMLateOubi2YPqEfC1dG8OjsHzmbkMqnL47D09X0vnGw15GYms1ny3aSlmX62Js1cQhhbYOZ/dUaHpz1PREn41nw0n34eriYMxWTRrRqwSsD+vHprgju+e5HolJTWfTAOLycqj72/rtnH/f/+AujFn/P8hMneXvEUPo0unrsPdWtK+FdOvH6+k3c++MSCktLWXT/OOxsbCyVlsFdXVvw/Ph+fP1HBI/M/ZEzCal8OqPq/RfSsgHr9p7m6XeXMfGtn0nOyOWzF8YZ7Zvw4V2ZMLgT877fxONvLqGouJRPXxiHnc7y+Y1s0ZJX+/ZjfsQeRi35gcjUVL4bW3XtyCouYsG+vYz75WeG//gdy06e4N0hw+jb8Or+c7K1Zf/FRN7ZucNSaQA1bCq2bdvG1KlTiYiIYMOGDZSWljJkyBDy8/PNFZ+RB8Z2ZfWao6zZcJz48+l88Ok6iopLGTGkvcnxUWeS+OKbrWzeFklJaXmV23V0sGXWS6N5b/5acvOKzBX+TU1qH8ovkcdYdvoEZ7PSeWX7egrLSnmgVTuT4yMuJbAu7izRWRmcz8li0YlDRKWn0jWgvtG44vJyUgvzDY+cEsv/gwsw4e5Q/txwjL83nyDuQjrvfbGeouJSRg0ynV9UdBL//W4bm3ZGUVpmev89PC6MlLRc5n22lsizSVxKyWb/0TguJmWZMRPTJnUK4ZeTx1kWeZLozAxe3bKhYv+1MX18RiReYN25aGIyMzifk82io4eJSksltJ7x/vN3dmFOv4H83/q/KdPrLZGK6qxdOx4aGsLKbSf4c+dJYi9mMO+7jRSVlHF3X9PH3qnYZD5Zup0Ne09TYuLYs7fVMSC0OZ/8uoPDZxK5kJLF1yv3kJCSxb0DO5g7nUqeCA1h6bETLD9xkuj0DF5bt5HC0jLub286v70JF9hwNpqYjAzOZ2Xz3cHDnE5NJaTB1WPv8dDOLNizl43RMZxOTePFv9bi7+LCXc2bWSotg4eHhrBy+zX77/vL+6+P6fxe+3oNv205ypmEVOKTMnlz0QY0Gg3d2gQZxjx4V2e++XMv247EEH0hjdcXrsXXw4X+XSyf36QuISw9cZzfTp0kOiODVzdV1I7725quHXsvXGB9zOXakZ3N4iOXa0fg1f23IiqST/dGsDMh3lJpADVsKtauXcvjjz9O27Zt6dixI4sXL+b8+fMcPFj5lLTadDotLZoHcPDI1RdIUeDgkTjatq5/gzVvbvrUu9izP8Zo25Zmq9XS3jeAnYnX5AfsvBBPF//Aam2jV/1gmnh4svfSBaPl3QODOPjYFDaPf5I3e9+Fh72DmqFXi06npUXTAA4cNd5/B47F07Zl9fIzpVfXpkRFJ/Hvl+7mz8VT+PaDxxh9l+WLuq1WSzs/f3YlnDcsU4BdCefpElCvWtvo2SCYJp5e7LuYaFimAT66azhfHdrP2Yx0laO2HKvWDhstrRr5s++U8bG372Q87ZtWb99cz8ZGg85GS0lJmdHy4pIyOrW4tXpUU7ZaLe0C/NkVZ1w7dsfH0zmwevn1CA6isacX+xMqakeQuzt+Li7sjr96POeVlHD0UlK1t6kWnY2WVg392Xv9/jsVT4dq7j8Hex06Gxuy8ys+NNb3dcfHw4V9p67ml19YwolzSbU+JmrrSu3YeX3tOH+eLvWqWTuCLteOxMSbDzazW5pTkZ2dDYCXl1eVY4qLiykuvvrJOCcnp1bP5e7mhM5GS2am8SebzMwCght412qbAAP7taZF0wAm/993td6GGjwdHNFptaQVFhgtTyssoKlH1a+vq50dex95BjutDeWKwms7Nxg1JtsSYlkbe4aE3Gwaunnwcrc+fDfiPsau/Am9opgtn+u5uzqis9GSkW2cX0ZWAQ3rV53fzQT6ezBmWCeW/nGA73+LoHWzAKY/OZDSsnLWbjl5q2FXm6fj5f1XYHx8phYU0NTzxvsvYuJk7Gxs0CsKs7ZuMvpk8UxIN8oUPYuOHq5yG3eim9UOteoGgEdVx15OAY3q1e7YKygq5djZizx5T3diL2WQkV3A0O6taN+sHheSs2oda214OlUce+kF19WO/AKa3KA2u9jZsWvKU4Zjb/aGTey63ET4ODsZtmG8zXx8XZxVzuDGDPsvp/b779n7+pCWlce+kxX5ebtV5JdeaZv5eLtbNr+qakdaQQFNb7D/XO3s2DPpau14bfMmdp633gfjK2rdVOj1eqZPn06vXr1o1870KSiouJY6d+7c2j6NWfn6uPLs5EG88MrSG14euZ3llZQw/LfvcLa1o1f9YGb1GMD5nGwiLiUA8GdMlGHs6Yw0ItNT2fnQU/QIDGJX4vmqNnvH0Go0RMUk8dVPFdcNz8am0DjYhzFDO1m0qaitvJISRvzyA862tvQMCua1Pv1IyMkiIvEC7Xz9mNixCyOX/mDtMFVVndpxO9eNK17/ag2vPzmUNR9Ppqxcz+n4FNZHnKZVIz9rh1Yt+SUl3L34R5zsbOnZMJhXBvQjISubvQkXbr7yHSR8RFeGdGvF5Hd/NXkp606VV1LCyJ9+wMnOll5Bwczq14/zOVnsvWDd/VfrpmLq1KmcOHGCnTt33nDczJkzmTFjhuHnnJwcgoKCbrCGadk5BZSV6/H0NO4iPT2dyMis3XXZls0D8PJ05uvPHjcs09lo6dguiLGju3DX3e+j11vm03xmUSFlej0+jsaTFn0cnUgtrDo/BYjPyQLgVHoKzTy8mdI5zNBUXC8hN5v0wgIaunlatKnIzi2krFyP13WTMr08nEjPqv119fTMPOISjC8LxF/IoH+PFrXeZm1kFl7ef07Gx6evkxOpBTfZf9lZAJxKS6WZpzdTQsKISLxAt8AGeDs5sfvxpwzjdVotr/buxxOdutD7u4XmSMXsqlM71KobAFlVHXtuTqRn1/7YS0zNZvLbv+Jgp8PZ0Z707Hz+88xIElOza73N2sgsqDj2vJ2uqx3OTqTdYM6KAsRnZQEQmZJKU28vnu7ejb0JFwxnKHycnUi9Zhs+zs6cSrbsZHbD/nOr+f57ZGgIj4/oypT3lxN9Ic2w/MoZCu/rtuHl5syZ85bNr6ra4eNk/Npf79raEZmaSjMvb6Z0DbN6U1Gr76mYNm0aq1evZsuWLTRo0OCGY+3t7XFzczN61EZZmZ4zZ5MI6XR1dqtGA106NeJkZO2uIx08Es/jT3/DpKmLDI+oM5fYuOUkk6YuslhDAVCq13M8NYle9a/JD+hVvyGHki9WeztajeaGs7MDnF3wdHAkpeDGd8OoraxMz5mYJEI6GO+/kPYNOXm6+vld73hUIsHXXT4JCvQkKbX2p8tro1Sv50RKMj0bBBuWaai41nkoyfQtz6Zcu/9+P32KYUu+Y8TP3xseSXm5fHX4AI+tWq52ChZR3dqhVt0AKCvXExWXTNc21+wbDXRtE8zxmOrvm6oUlZSRnp2Pq5M93ds3ZPuhmFveZk2U6vWcSEqmZ8Prjr2GwRy+WP38NFw99hKys0nJyzPapoudHR3rBdRom2ooK9cTFZ9Mt9bX7b/WwRy7wf57bFgok0Z359kPVxAZZ3ybb2JqNmlZeUbHhLODHe2aBKhyTNTEldrRK8hE7bhUg/13k9pvKTU6U6EoCs8++ywrVqxg69atNG7c2FxxmfTriv3MfGEkUWeTiDp9ifvGhOJob8uaDccBeOWFkaSm5/L14u1AxeTARsEV9ynb6rT4eLvQrIkfhYUlJF7KorCwhNj4NKPnKCwqJTu3qNJyS1h4/AAf9B/BsdQkjqZc4on2oTjZ2rLs9AkAPhwwgqT8XN7dV3Gqf0qnMI6lJhGfk4W9jQ0DgpswtnkbZu3cAICTzpbpoT1Zc+4MqQX5NHT3YGZYP+KyM9meEGfx/H754wCvPjeCqJgkIs9e4oFRoTg62PLXpor8Zj03gtSMXL78sSI/nU5LowZX9p8Nvt4uNGvkR2FRCYmX7+5Y+udBvpj3EI/eG8bmXadp07wedw/pwLufr7d4fguPHOSDwcM4npLEkeQknuzUBSedLctOVeT3wV3DSM7L4909FZ/Qp4R041hKMvHZWdjZ2DCgUWPGtmzNrK0V35GSVVREVpHx3Uhlej2p+fmcy8q0bHK3yNq1Y8m6g8z+xzAiY5M5eS6JB4d0wdHelj93VFwim/OPYaRm5rHgt4p9o7PR0qR+xVwtWxsbfD1daRHsS0FRKRcuf49B93YN0Wg0xF/KoIG/B/83vi9xlzL5Y6flL7t9e+Ag740YxvGkZI5dSuLx0C442try2/GKWN4bUXHsvb+9Ir+nw7pyPCmZ81nZ2NnY0L9pY8a0bc3sDVe/n2fxgcNM6RFGXGYmCVk5PN+nJ8l5eWw4G23x/H5ad5A5k4ZxKi6Zk7FJPHTX5f13+bWeO2kYKZl5LFhekV/48K5MHtODWV+t4VJatmEORUFxKYXFpQD8vOEwT44KIyE5k8TUHJ4Z25PUrDy2HrJ8fgsPHeSDIcM4lpzE0aQknujSBSdbW367UjuGDCMpP4/3dlXk90zXbhxPTiY+63LtaNyYsa1a89rmq/vP3d6BQDdX/J0rbqNtcnluV2p+PmnXzb9RU42aiqlTp7JkyRJWrVqFq6srSZe/WMXd3R3HKu6nVdOW7VF4uDvxxCO98fJyJjomhZde+5XMrIoXyM/PzWjyoY+XC98smGj4+cH7wnjwvjAOHzvP9H/+bPZ4a2p1zGm8HZyYEdoLXydnTqWl8Njfvxkmbwa6uBrl52Rry5t97qKeswtFZWXEZGUwfctfrI6puP+/XFFo5eXLvS3a4mbnQHJBHjsuxPHB/p2U6C1/bXHzrtN4uDkxaUIvvDydiY5N4YU3fiPz8gQ6f1/j/Hw8XVj8Ubjh54fGdOOhMd04fOI8z762FKi47fSVd1Yy+ZG+PP5ATy6lZPPJt1vYsN30F0iZ0+qzp/FydOT5sF74OjsRmZpK+B/LDfuvvosbyjX5Odra8u/+g6jncnn/ZWby/IY1rD5rue9vsBRr144N+87g4erE5LE98XZ34sz5VJ774HfD5L8Ab1ejfePr6cJPbzxq+PnR4aE8OjyUg1EJPP32MgBcHO2Zen9v/DxdyMkvYvOBaP67fCfl5Za/7ffvqDN4OzoxvXdPfJ2dOJWSyhPLfjdM3gx0M35vOdraMnfIIAJcXCkqK+NcRgYv/LWGv6POGMZ8tW8/jna2vDnkLtwc7DlwIZEnlv1OSbnla8eG/WfwdHXi6TGX919CKs9+dM3+83I1OrN874AO2NnqeHfqaKPtfLVqD1+t2gPAd2v242Bvyyvhd/H/7dw/bxNnHAfwn10XK5FsI8FCFE/ZOlRihE78EUIVQ1c2vwKkdqkqVUpVqRuqQGLPK6DqgoA3AIxsbelS1SEL0CjOEKuEuw5tAFWV6ojnnssdn8+WyPLz/HS+b77nszNY7sfjX5/Gte9/qOVzF3ee/BInlpbiizOfxMnl5fjp+bOY/Hj79R//leEwingr+3sfxrfnLsSpwT/Z8cd2fH7/btx58iY7Lq6txfVLl1//fOvTKxERcePRg7j56GFls3TKcvGvAHQ6nf/8/cbGRkwmk4WeYzabxWg0irMXv4leL/9XG3P47bN2//fz8b26d1C9zfPtPIbF3jymX34dOzs773RL4bDeNTsOcuP01e/ig2PtzI3tj+reQbWO//z/j2m6Fx/nu2WeUzGfx+9fLZYbh779AXBYsgPeD+28HAMAslMqAIAklAoAIAmlAgBIQqkAAJJQKgCAJJQKACAJpQIASEKpAACSUCoAgCSUCgAgCaUCAEhCqQAAklAqAIAklAoAIAmlAgBIQqkAAJJQKgCAJJQKACAJpQIASKKXe8GyLCMiYn9/nnvpbIq9dne1/Zd176B6bT2Gxfzv8+7gPGyKg/2+etni3GjvaBER8erPundQvWLerPNqUYfJjU6ZOV02NzdjPB7nXBL4l+l0Gqurq3VvY2FyA+q3SG5kLxVFUcTW1lYMBoPodDqVrzebzWI8Hsd0Oo3hcFj5ermZr9lyz1eWZezu7sbKykp0u815N0ZupGW+ZjvKuZH99ke3263lCmk4HLbyxXXAfM2Wc77RaJRlnZTkRjXM12xHMTeac6kCABxpSgUAkETrS0W/34/19fXo9/t1b6US5mu2ts/XVG0/LuZrtqM8X/YPagIA7dT6dyoAgDyUCgAgCaUCAEhCqQAAklAqAIAklAoAIAmlAgBIQqkAAJL4Cxne7bqCAm7/AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.531136Z",
     "iopub.status.busy": "2025-02-09T08:41:15.530891Z",
     "iopub.status.idle": "2025-02-09T08:41:15.538877Z",
     "shell.execute_reply": "2025-02-09T08:41:15.538467Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.531120Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.539478Z",
     "iopub.status.busy": "2025-02-09T08:41:15.539318Z",
     "iopub.status.idle": "2025-02-09T08:41:15.544244Z",
     "shell.execute_reply": "2025-02-09T08:41:15.543825Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.539457Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.544958Z",
     "iopub.status.busy": "2025-02-09T08:41:15.544730Z",
     "iopub.status.idle": "2025-02-09T08:41:15.550019Z",
     "shell.execute_reply": "2025-02-09T08:41:15.549610Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.544943Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.552699Z",
     "iopub.status.busy": "2025-02-09T08:41:15.552359Z",
     "iopub.status.idle": "2025-02-09T08:41:15.556829Z",
     "shell.execute_reply": "2025-02-09T08:41:15.556236Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.552682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.557621Z",
     "iopub.status.busy": "2025-02-09T08:41:15.557446Z",
     "iopub.status.idle": "2025-02-09T08:41:15.682791Z",
     "shell.execute_reply": "2025-02-09T08:41:15.682339Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.557604Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:15.683731Z",
     "iopub.status.busy": "2025-02-09T08:41:15.683309Z",
     "iopub.status.idle": "2025-02-09T08:41:16.101004Z",
     "shell.execute_reply": "2025-02-09T08:41:16.100537Z",
     "shell.execute_reply.started": "2025-02-09T08:41:15.683713Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.101908Z",
     "iopub.status.busy": "2025-02-09T08:41:16.101641Z",
     "iopub.status.idle": "2025-02-09T08:41:16.116511Z",
     "shell.execute_reply": "2025-02-09T08:41:16.116044Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.101891Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.117210Z",
     "iopub.status.busy": "2025-02-09T08:41:16.117052Z",
     "iopub.status.idle": "2025-02-09T08:41:16.121378Z",
     "shell.execute_reply": "2025-02-09T08:41:16.120913Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.117194Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.122133Z",
     "iopub.status.busy": "2025-02-09T08:41:16.121984Z",
     "iopub.status.idle": "2025-02-09T08:41:16.125499Z",
     "shell.execute_reply": "2025-02-09T08:41:16.124972Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.122118Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.126153Z",
     "iopub.status.busy": "2025-02-09T08:41:16.125969Z",
     "iopub.status.idle": "2025-02-09T08:41:16.211225Z",
     "shell.execute_reply": "2025-02-09T08:41:16.210851Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.126138Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.211842Z",
     "iopub.status.busy": "2025-02-09T08:41:16.211692Z",
     "iopub.status.idle": "2025-02-09T08:41:16.215221Z",
     "shell.execute_reply": "2025-02-09T08:41:16.214812Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.211827Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.216127Z",
     "iopub.status.busy": "2025-02-09T08:41:16.215781Z",
     "iopub.status.idle": "2025-02-09T08:41:16.220349Z",
     "shell.execute_reply": "2025-02-09T08:41:16.219918Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.216110Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"test_2.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.221231Z",
     "iopub.status.busy": "2025-02-09T08:41:16.220894Z",
     "iopub.status.idle": "2025-02-09T08:41:16.224473Z",
     "shell.execute_reply": "2025-02-09T08:41:16.224075Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.221214Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.225143Z",
     "iopub.status.busy": "2025-02-09T08:41:16.225000Z",
     "iopub.status.idle": "2025-02-09T08:41:16.228846Z",
     "shell.execute_reply": "2025-02-09T08:41:16.228296Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.225129Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.229761Z",
     "iopub.status.busy": "2025-02-09T08:41:16.229605Z",
     "iopub.status.idle": "2025-02-09T08:41:16.236312Z",
     "shell.execute_reply": "2025-02-09T08:41:16.235835Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.229746Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.237088Z",
     "iopub.status.busy": "2025-02-09T08:41:16.236933Z",
     "iopub.status.idle": "2025-02-09T08:41:16.394523Z",
     "shell.execute_reply": "2025-02-09T08:41:16.393892Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.237073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 1024, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.2, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 4, # 可调整\n",
    "    \"num_decoder_layers\": 4, # 可调整\n",
    "    \"num_encoder_layers\": 4, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": True,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 8192\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:16.395257Z",
     "iopub.status.busy": "2025-02-09T08:41:16.395052Z",
     "iopub.status.idle": "2025-02-09T08:41:17.405426Z",
     "shell.execute_reply": "2025-02-09T08:41:17.404986Z",
     "shell.execute_reply.started": "2025-02-09T08:41:16.395241Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 102497280\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T08:41:17.406189Z",
     "iopub.status.busy": "2025-02-09T08:41:17.405952Z",
     "iopub.status.idle": "2025-02-09T09:24:14.659892Z",
     "shell.execute_reply": "2025-02-09T09:24:14.659289Z",
     "shell.execute_reply.started": "2025-02-09T08:41:17.406172Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18999it [42:55,  7.38it/s, epoch=68, loss=2.19]                      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 69 / global_step 19000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-09T09:24:14.661483Z",
     "iopub.status.busy": "2025-02-09T09:24:14.660862Z",
     "iopub.status.idle": "2025-02-09T09:24:14.844363Z",
     "shell.execute_reply": "2025-02-09T09:24:14.843909Z",
     "shell.execute_reply.started": "2025-02-09T09:24:14.661449Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/test_2.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-09T09:24:14.845441Z",
     "iopub.status.busy": "2025-02-09T09:24:14.845027Z",
     "iopub.status.idle": "2025-02-09T09:24:23.128316Z",
     "shell.execute_reply": "2025-02-09T09:24:23.127730Z",
     "shell.execute_reply.started": "2025-02-09T09:24:14.845409Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "10it [00:00, 97.79it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "25it [00:00, 124.52it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:06, 145.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 2.7916602045606136\n",
      "testing bleu: 0.7063209432131355\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T09:24:23.129668Z",
     "iopub.status.busy": "2025-02-09T09:24:23.129060Z",
     "iopub.status.idle": "2025-02-09T09:24:23.132187Z",
     "shell.execute_reply": "2025-02-09T09:24:23.131668Z",
     "shell.execute_reply.started": "2025-02-09T09:24:23.129635Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-09T09:24:23.133364Z",
     "iopub.status.busy": "2025-02-09T09:24:23.133101Z",
     "iopub.status.idle": "2025-02-09T09:24:23.429374Z",
     "shell.execute_reply": "2025-02-09T09:24:23.428922Z",
     "shell.execute_reply.started": "2025-02-09T09:24:23.133336Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "ExecutionIndicator": {
     "show": false
    },
    "execution": {
     "iopub.execute_input": "2025-02-09T09:24:23.430206Z",
     "iopub.status.busy": "2025-02-09T09:24:23.429905Z",
     "iopub.status.idle": "2025-02-09T09:24:24.665057Z",
     "shell.execute_reply": "2025-02-09T09:24:24.664389Z",
     "shell.execute_reply.started": "2025-02-09T09:24:23.430189Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  5%|▌         | 7/128 [00:00<00:01, 76.91it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['a man and a woman eating dinner']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    # \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake. ans:['man in a white boat on a boat.']\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach. ans:['a man with a girl and a girl on the beach with a fishing pole.']\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race. ans:['three men on horses are running in a race.']\n",
    "    \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐 # ['a man and a woman are eating food.']\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
